Multi-Sensor Integrated Navigation Systems

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closed (31 October 2024)

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Topic Information

Dear Colleagues, 

Multi-sensor integrated navigation systems have become a hot area of research and have made significant progress in both theoretical investigation and practical applications. Various intelligent and advanced multi-sensor fusion and data analysis algorithms and technologies have been applied in many fields, such as sensor networks, automation, monitoring, micro/nano systems, operation, control, field robotics, navigation and autonomous vehicles. In all these applications, the key enabling technologies focus on localization and navigation. 

Many recent studies have been focused on the integration of GNSS systems with a variety of underlying sensor technologies, such as MEMS IMU, RF (Radio Frequency), cameras, ultrasonic sensors, laser scanners, magnetometers, and barometers. The fusion scheme can use all data from these sensors and overcome their limitations, which provides a reliable and precise navigation. The multi-dimensional capabilities of the recent mobile computing of inexpensive devices (including smartphones, tablets, and wearables) has shown the way for new personalized services, such as indoor localization, which nowadays represents a major scientific and technological challenge. 

Multi-sensor integrated navigation systms have become key to add location and motion context to data in various areas, from robots and self-driving cars to smartphones and internet-of-things (IoT) devices. In recent years, technological advancements have facilitated the manufacturing of compact, inexpensive, and low-power navigation and sensing (e.g., inertial navigation, computer vision, LiDAR, wireless, magnetic, light, and sound) sensors for smart devices. These advances have led to the fast development of navigation sensors, data processing, and related services. Multi-sensor integrated navigation is a mainstream direction to design an accurate, low-cost, low-power, reliable, and scalable navigation system for cutting-edge applications. Extensive research efforts have been paid to multi-sensor integrated navigation algorithms, architectures, and systems. 

This Topic is devoted to new advances and research results on multi-sensor fusion and data analysis to attract widespread attention to the many research fields that apply various positioning methods in indoor and outdoor environments. The applications of various multi-sensor fusion technologies and of various systems are also welcome. This Topic will also consider articles introducing novel ideas and algorithms and the latest advances in the field of multi-sensor data fusion coupled with any prototype implementations and evaluations. 

This Topic include, but are not limited to: 

GNSS and RF Theory, New Technologies and Algorithms

• GNSS/IMU conbimed system for Aeronautical and Astronautical Engineering;
• GNSS theories and applications for Sea and Ocean;
• GNSS Continuously Operating Reference Stations (CORS);
• New GNSS  Ionospheric and Tropospheric technologies and algorithms;
• GNSS Metereology;
• New GNSS technologies for monitering wind power generation system, wild life, etc.;
• High precision mobile phone positioning based on GNSS, IMU, and other sensors;
• WiFi, IMU, LiDAR, and camera sensors for pedestrian navigation;
• Bluetooth Angle of Arrival (AOA) for positioning and navigation;
• UWB-based locating systems;
• GNSS, IMU, and camera sensors for live entertainment, 3D tourism devices, etc.;
• GNSS for Transportation, Agriculture, Forestry, Fishery, Mining and other projects;
• Precise Point Positioning (PPP): new technologies and algorithms. 

Multi-sensor Fusion System

• Multi-sensor fusion for precise navigation;
• Sensor signal processing and data analysis;
• Applications of machine learning;
• Optimal sensor placement;
• Multi-sensor fusion for 3D localization;
• Intelligent multi-sensor fusion for indoor navigation;
• Multi-sensor-based monitoring and operation, and simultaneous localization and mapping (SLAM);
• Multi-sensor-based control system for machine, vehicles, etc.;
• Localization and Internet of Things;
• Modelling and analysis of multi-sensors;
• Software and hardware development for multi-sensor fusion;
• Algorithms to combine sensors for pedestrian navigation or localization;
• Automatic indoor mapping for navigation and tracking systems and smartphone multi-sensor fusion. 

Wearable Navigation and Unmanned Navigation

• New wearable navigation and sensing sensors and applications;
• Algorithms and systems for smart wearable positioning;
• Data-driven wearable navigation;
• Interaction between smart wearables and robots;
• Combination of wearable navigation and autonomous driving;
• Application of smart wearable motion sensing in medical health;
• The use of smart wearable navigation in public safety;
• Smart wearable AR and VR;
• Geoinformation systems on data from wearable devices;
• Unmanned Aerial Vehicle (UAV) with multi-sensors.

Prof. Dr. Hang Guo
Dr. Marcin Uradzinski
Prof. Dr. You Li
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Aerospace aerospace	2.1	3.4	2014	24 Days	CHF 2400	Submit
Applied Sciences applsci	2.5	5.3	2011	17.8 Days	CHF 2400	Submit
Remote Sensing remotesensing	4.2	8.3	2009	24.7 Days	CHF 2700	Submit
Sensors sensors	3.4	7.3	2001	16.8 Days	CHF 2600	Submit
Smart Cities smartcities	7.0	11.2	2018	25.8 Days	CHF 2000	Submit

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Published Papers (30 papers)

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21 pages, 946 KiB  

Open AccessArticle

Sequential Mixed Cost-Based Multi-Sensor and Relative Dynamics Robust Fusion for Spacecraft Relative Navigation

by Shoupeng Li and Weiwei Liu

Remote Sens. 2024, 16(23), 4384; https://doi.org/10.3390/rs16234384 - 23 Nov 2024

Viewed by 359

Abstract

The non-redescending convex functions degrade the filtering robustness, whereas the redescending non-convex functions improve filtering robustness, but they tend to converge towards local minima. This work investigates the properties of convex and non-convex cost functions from robustness and stability perspectives, respectively. To improve [...] Read more.

The non-redescending convex functions degrade the filtering robustness, whereas the redescending non-convex functions improve filtering robustness, but they tend to converge towards local minima. This work investigates the properties of convex and non-convex cost functions from robustness and stability perspectives, respectively. To improve filtering robustness and stability to the high level of non-Gaussian noise, a sequential mixed convex and non-convex cost strategy is presented. To avoid the matrix singularity induced by applying the non-convex function, the M-estimation type Kalman filter is transformed into its information filtering form. Further, to address the problem of the estimation consistency in the iterated unscented Kalman filter, the iterated sigma point filtering framework is adopted using the statistical linear regression method. The simulation results show that, under different levels of heavy-tailed non-Gaussian noise, the mixed cost strategy can avoid the non-convex function-based filters falling into the local minimum, and further can improve the robustness of the convex function-based filter. Therefore, the mixed cost strategy provides a comprehensive improvement in the efficiency of the robust iterated filter. Full article

(This article belongs to the Topic Multi-Sensor Integrated Navigation Systems)

18 pages, 3786 KiB  

Open AccessArticle

A Multi-Sensor Fusion Underwater Localization Method Based on Unscented Kalman Filter on Manifolds

by Yang Wang, Chenxi Xie, Yinfeng Liu, Jialin Zhu and Jixing Qin

Sensors 2024, 24(19), 6299; https://doi.org/10.3390/s24196299 - 29 Sep 2024

Viewed by 3108

Abstract

In recent years, the simplified computation of position and velocity changes in nonlinear systems using Lie groups and Lie algebra has been widely used in the study of robot localization systems. The unscented Kalman filter (UKF) can effectively deal with nonlinear systems through [...] Read more.

In recent years, the simplified computation of position and velocity changes in nonlinear systems using Lie groups and Lie algebra has been widely used in the study of robot localization systems. The unscented Kalman filter (UKF) can effectively deal with nonlinear systems through the unscented transformation, and in order to more accurately describe the robot localization system, the UKF method based on Lie groups has been studied successively. The computational complexity of the UKF on Lie groups is high, and in order to simplify its computation, the Lie groups are applied to the manifold, which efficiently handles the state and uncertainty and ensures that the system maintains the geometric constraints and computational simplicity during the updating process. In this paper, a multi-sensor fusion localization method based on an unscented Kalman filter on manifolds (UKF-M) is investigated. Firstly, a system model and a multi-sensor model are established based on an Autonomous Underwater Vehicle (AUV), and a corresponding UKF-M is designed for the system. Secondly, the multi-sensor fusion method is designed, and the fusion method is applied to the UKF-M. Finally, the proposed method is validated using an underwater cave dataset. The experiments demonstrate that the proposed method is suitable for underwater environments and can significantly correct the cumulative error in the trajectory estimation to achieve accurate underwater localization. Full article

(This article belongs to the Topic Multi-Sensor Integrated Navigation Systems)

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22 pages, 61523 KiB  

Open AccessArticle

Aerial Hybrid Adjustment of LiDAR Point Clouds, Frame Images, and Linear Pushbroom Images

by Vetle O. Jonassen, Narve S. Kjørsvik, Leif Erik Blankenberg and Jon Glenn Omholt Gjevestad

Remote Sens. 2024, 16(17), 3179; https://doi.org/10.3390/rs16173179 - 28 Aug 2024

Viewed by 642

Abstract

In airborne surveying, light detection and ranging (LiDAR) strip adjustment and image bundle adjustment are customarily performed as separate processes. The bundle adjustment is usually conducted from frame images, while using linear pushbroom (LP) images in the bundle adjustment has been historically challenging [...] Read more.

In airborne surveying, light detection and ranging (LiDAR) strip adjustment and image bundle adjustment are customarily performed as separate processes. The bundle adjustment is usually conducted from frame images, while using linear pushbroom (LP) images in the bundle adjustment has been historically challenging due to the limited number of observations available to estimate the exterior image orientations. However, data from these three sensors conceptually provide information to estimate the same trajectory corrections, which is favorable for solving the problems of image depth estimation or the planimetric correction of LiDAR point clouds. Thus, our purpose with the presented study is to jointly estimate corrections to the trajectory and interior sensor states in a scalable hybrid adjustment between 3D LiDAR point clouds, 2D frame images, and 1D LP images. Trajectory preprocessing is performed before the low-frequency corrections are estimated for certain time steps in the following adjustment using cubic spline interpolation. Furthermore, the voxelization of the LiDAR data is used to robustly and efficiently form LiDAR observations and hybrid observations between the image tie-points and the LiDAR point cloud to be used in the adjustment. The method is successfully demonstrated with an experiment, showing the joint adjustment of data from the three different sensors using the same trajectory correction model with spline interpolation of the trajectory corrections. The results show that the choice of the trajectory segmentation time step is not critical. Furthermore, photogrammetric sub-pixel planimetric accuracy is achieved, and height accuracy on the order of mm is achieved for the LiDAR point cloud. This is the first time these three types of sensors with fundamentally different acquisition techniques have been integrated. The suggested methodology presents a joint adjustment of all sensor observations and lays the foundation for including additional sensors for kinematic mapping in the future. Full article

(This article belongs to the Topic Multi-Sensor Integrated Navigation Systems)

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20 pages, 3776 KiB  

Open AccessArticle

An Integrated Navigation Method Aided by Position Correction Model and Velocity Model for AUVs

by Pengfei Lv, Junyi Lv, Zhichao Hong and Lixin Xu

Sensors 2024, 24(16), 5396; https://doi.org/10.3390/s24165396 - 21 Aug 2024

Viewed by 3438

Abstract

When autonomous underwater vehicles (AUVs) perform underwater tasks, the absence of GPS position assistance can lead to a decrease in the accuracy of traditional navigation systems, such as the extended Kalman filter (EKF), due to the accumulation of errors. To enhance the navigation [...] Read more.

When autonomous underwater vehicles (AUVs) perform underwater tasks, the absence of GPS position assistance can lead to a decrease in the accuracy of traditional navigation systems, such as the extended Kalman filter (EKF), due to the accumulation of errors. To enhance the navigation accuracy of AUVs in the absence of position assistance, this paper proposes an innovative navigation method that integrates a position correction model and a velocity model. Specifically, a velocity model is developed using a dynamic model and the Optimal Pruning Extreme Learning Machine (OP-ELM) method. This velocity model is trained online to provide velocity outputs during the intervals when the Doppler Velocity Log (DVL) is not updating, ensuring more consistent and reliable velocity estimation. Additionally, a position correction model (PCM) is constructed, based on a hybrid gated recurrent neural network (HGRNN). This model is specifically designed to correct the AUV’s navigation position when GPS data are unavailable underwater. The HGRNN utilizes historical navigation data and patterns learned during training to predict and adjust the AUV’s estimated position, thereby reducing the drift caused by the lack of real-time position updates. Experimental results demonstrate that the proposed VM-PCM-EKF algorithm can significantly improve the positioning accuracy of the navigation system, with a maximum accuracy improvement of 87.2% compared to conventional EKF algorithms. This method not only improves the reliability and accuracy of AUV missions but also opens up new possibilities for more complex and extended underwater operations. Full article

(This article belongs to the Topic Multi-Sensor Integrated Navigation Systems)

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17 pages, 13009 KiB  

Open AccessArticle

A Near-Vertical Well Attitude Measurement Method with Redundant Accelerometers and MEMS IMU/Magnetometers

by Shaowen Ji, Chunxi Zhang, Shuang Gao and Aoxiang Lian

Appl. Sci. 2024, 14(14), 6138; https://doi.org/10.3390/app14146138 - 15 Jul 2024

Viewed by 3050

Abstract

Vertical drilling is the first stage of petroleum exploitation and directional well technology. The near-vertical attitude at each survey station directly determines the whole direction accuracy of the borehole trajectory. However, the attitude measurement for near-vertical wells has poor azimuth accuracy because the [...] Read more.

Vertical drilling is the first stage of petroleum exploitation and directional well technology. The near-vertical attitude at each survey station directly determines the whole direction accuracy of the borehole trajectory. However, the attitude measurement for near-vertical wells has poor azimuth accuracy because the poor signal-to-noise ratio of radial accelerometers hardly obtains the correct horizontal attitude, especially the roll angle. In this paper, a novel near-vertical attitude measurement method was proposed to address this issue. The redundant micro-electromechanical system (MEMS) accelerometers were employed to replace the original accelerometers from MEMS inertial measurement unit (IMU)/magnetometers for calculating horizontal attitude under near-vertical conditions. In addition, a simplified four-position calibration method for the redundant accelerometers was proposed to compensate for the installation and non-orthogonal error. We found that the redundant accelerometers enhanced the signal-to-noise ratio to upgrade the azimuth accuracy at the near-vertical well section. Compared with the traditional method, the experiment results show that the average azimuth errors and roll errors are reduced from 34.45° and 27.09° to 5.7° and 0.61°, respectively. The designed configuration scheme is conducive to the miniaturized design and low-cost requirements of wellbore measuring tools. The proposed attitude measurement method can effectively improve the attitude accuracy of near-vertical wells. Full article

(This article belongs to the Topic Multi-Sensor Integrated Navigation Systems)

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18 pages, 4121 KiB  

Open AccessArticle

A Combined UWB/IMU Localization Method with Improved CKF

by Pengfei Ji, Zhongxing Duan and Weisheng Xu

Sensors 2024, 24(10), 3165; https://doi.org/10.3390/s24103165 - 16 May 2024

Cited by 1 | Viewed by 1264

Abstract

Aiming at the problem that ultra-wide band (UWB) cannot be accurately localized in environments with large noise variations and unknown statistical properties, a combinatorial localization method based on improved cubature (CKF) is proposed. First, in order to overcome the problem of inaccurate local [...] Read more.

Aiming at the problem that ultra-wide band (UWB) cannot be accurately localized in environments with large noise variations and unknown statistical properties, a combinatorial localization method based on improved cubature (CKF) is proposed. First, in order to overcome the problem of inaccurate local approximation or even the inability to converge due to the initial value not being set near the optimal solution in the process of solving the UWB position by the least-squares method, the Levenberg–Marquardt algorithm (L–M) is adopted to optimally solve the UWB position. Secondly, because UWB and IMU information are centrally fused, an adaptive factor is introduced to update the measurement noise covariance matrix in real time to update the observation noise, and the fading factor is added to suppress the filtering divergence to achieve an improvement for the traditional CKF algorithm. Finally, the performance of the proposed combined localization method is verified by field experiments in line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios, respectively. The results show that the proposed method can maintain high localization accuracy in both LOS and NLOS scenarios. Compared with the Extended Kalman filter (EKF), unbiased Kalman filter (UKF), and CKF algorithms, the localization accuracies of the proposed method in NLOS scenarios are improved by 25.2%, 18.3%, and 11.3%, respectively. Full article

(This article belongs to the Topic Multi-Sensor Integrated Navigation Systems)

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22 pages, 803 KiB  

Open AccessArticle

Three-Dimensional Signal Source Localization with Angle-Only Measurements in Passive Sensor Networks

by Linhai Wang, Shenghua Zhou, Min Gong, Pengfei Zhao, Jian Yang and Xin Sui

Remote Sens. 2024, 16(8), 1319; https://doi.org/10.3390/rs16081319 - 9 Apr 2024

Viewed by 1125

Abstract

Some passive sensors can provide only relative angles of a signal source. To obtain the signal source location, multiple passive sensors can be constructed into a passive sensor network through communication links. This paper investigates the source localization problem with angle-only measurements in [...] Read more.

Some passive sensors can provide only relative angles of a signal source. To obtain the signal source location, multiple passive sensors can be constructed into a passive sensor network through communication links. This paper investigates the source localization problem with angle-only measurements in three-dimensional space. First, we present an intersection localization method, which estimates the target position by minimizing the sum of distances between lines formed by angle-only measurements. It has the same target position estimate as the widely used least-squares (LS) method, but with a lower computational cost. Furthermore, considering the differences in measurement accuracy of sensors, the weighted least-squares (WLS) algorithm can achieve better localization performance than the LS method. Unfortunately, since the coefficient matrix and the noise vector are correlated, the WLS method is biased. The bias-compensation WLS (BCWLS) method is also presented in this paper to reduce the bias by estimating the correlation between the coefficient matrix and the pseudolinear noise vector. To evaluate the performance of the presented algorithms, numerical simulations are conducted, indicating that the superiority of the intersection localization method in computational cost and the superiority of the BCWLS method in localization accuracy. Full article

(This article belongs to the Topic Multi-Sensor Integrated Navigation Systems)

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20 pages, 3494 KiB  

Open AccessArticle

Visual–Inertial Odometry of Structured and Unstructured Lines Based on Vanishing Points in Indoor Environments

by Xiaojing He, Baoquan Li, Shulei Qiu and Kexin Liu

Appl. Sci. 2024, 14(5), 1990; https://doi.org/10.3390/app14051990 - 28 Feb 2024

Viewed by 929

Abstract

In conventional point-line visual–inertial odometry systems in indoor environments, consideration of spatial position recovery and line feature classification can improve localization accuracy. In this paper, a monocular visual–inertial odometry based on structured and unstructured line features of vanishing points is proposed. First, the [...] Read more.

In conventional point-line visual–inertial odometry systems in indoor environments, consideration of spatial position recovery and line feature classification can improve localization accuracy. In this paper, a monocular visual–inertial odometry based on structured and unstructured line features of vanishing points is proposed. First, the degeneracy phenomenon caused by a special geometric relationship between epipoles and line features is analyzed in the process of triangulation, and a degeneracy detection strategy is designed to determine the location of the epipoles. Then, considering that the vanishing point and the epipole coincide at infinity, the vanishing point feature is introduced to solve the degeneracy and direction vector optimization problem of line features. Finally, threshold constraints are used to categorize straight lines into structural and non-structural features under the Manhattan world assumption, and the vanishing point measurement model is added to the sliding window for joint optimization. Comparative tests on the EuRoC and TUM-VI public datasets validated the effectiveness of the proposed method. Full article

(This article belongs to the Topic Multi-Sensor Integrated Navigation Systems)

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24 pages, 5998 KiB  

Open AccessArticle

A Fast Self-Calibration Method for Dual-Axis Rotational Inertial Navigation Systems Based on Invariant Errors

by Xin Sun, Jizhou Lai, Pin Lyu, Rui Liu and Wentao Gao

Sensors 2024, 24(2), 597; https://doi.org/10.3390/s24020597 - 17 Jan 2024

Cited by 1 | Viewed by 1162

Abstract

In order to ensure that dual-axis rotational inertial navigation systems (RINSs) maintain a high level of accuracy over the long term, there is a demand for periodic calibration during their service life. Traditional calibration methods for inertial measurement units (IMUs) involve removing the [...] Read more.

In order to ensure that dual-axis rotational inertial navigation systems (RINSs) maintain a high level of accuracy over the long term, there is a demand for periodic calibration during their service life. Traditional calibration methods for inertial measurement units (IMUs) involve removing the IMU from the equipment, which is a laborious and time-consuming process. Reinstalling the IMU after calibration may introduce new installation errors. This paper focuses on dual-axis rotational inertial navigation systems and presents a system-level self-calibration method based on invariant errors, enabling high-precision automated calibration without the need for equipment disassembly. First, navigation parameter errors in the inertial frame are expressed as invariant errors. This allows the corresponding error models to estimate initial attitude even more rapidly and accurately in cases of extreme misalignment, eliminating the need for coarse alignment. Next, by utilizing the output of a gimbal mechanism, angular velocity constraint equations are established, and the backtracking navigation is introduced to reuse sensor data, thereby reducing the calibration time. Finally, a rotation scheme for the IMU is designed to ensure that all errors are observable. The observability of the system is analyzed based on a piecewise constant system method and singular value decomposition (SVD) observability analysis. The simulation and experimental results demonstrate that this method can effectively estimate IMU errors and installation errors related to the rotation axis within 12 min, and the estimated error is less than 4%. After using this method to compensate for the calibration error, the velocity and position accuracies of a RINS are significantly improved. Full article

(This article belongs to the Topic Multi-Sensor Integrated Navigation Systems)

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21 pages, 4555 KiB  

Open AccessArticle

PLI-SLAM: A Tightly-Coupled Stereo Visual-Inertial SLAM System with Point and Line Features

by Zhaoyu Teng, Bin Han, Jie Cao, Qun Hao, Xin Tang and Zhaoyang Li

Remote Sens. 2023, 15(19), 4678; https://doi.org/10.3390/rs15194678 - 24 Sep 2023

Cited by 6 | Viewed by 1780

Abstract

Point feature-based visual simultaneous localization and mapping (SLAM) systems are prone to performance degradation in low-texture environments due to insufficient extraction of point features. In this paper, we propose a tightly-coupled stereo visual-inertial SLAM system with point and line features (PLI-SLAM) to enhance [...] Read more.

Point feature-based visual simultaneous localization and mapping (SLAM) systems are prone to performance degradation in low-texture environments due to insufficient extraction of point features. In this paper, we propose a tightly-coupled stereo visual-inertial SLAM system with point and line features (PLI-SLAM) to enhance the robustness and reliability of systems in low-texture environments. We improve Edge Drawing lines (EDlines) for line feature detection by introducing curvature detection and a new standard for minimum line segment length to improve the accuracy of the line features, while reducing the line feature detection time. We contribute also with an improved adapting factor based on experiment to adjust the error weight of line features, which further improves the localization accuracy of the system. Our system has been tested on the EuRoC dataset. Tests on public datasets and in real environments have shown that PLI-SLAM achieves high accuracy. Furthermore, PLI-SLAM could still operate robustly even in some challenging environments. The processing time of our method is reduced by 28%, compared to the ORB-LINE-SLAM based on point and line, when using Line Segment Detector (LSD). Full article

(This article belongs to the Topic Multi-Sensor Integrated Navigation Systems)

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19 pages, 10042 KiB  

Open AccessArticle

InfoLa-SLAM: Efficient Lidar-Based Lightweight Simultaneous Localization and Mapping with Information-Based Keyframe Selection and Landmarks Assisted Relocalization

by Yuan Lin, Haiqing Dong, Wentao Ye, Xue Dong and Shuogui Xu

Remote Sens. 2023, 15(18), 4627; https://doi.org/10.3390/rs15184627 - 20 Sep 2023

Cited by 1 | Viewed by 1724

Abstract

This work reports an information-based landmarks assisted simultaneous localization and mapping (InfoLa-SLAM) in large-scale scenes using single-line lidar. The solution employed two novel designs. The first design was a keyframe selection method based on Fisher information, which reduced the computational cost of the [...] Read more.

This work reports an information-based landmarks assisted simultaneous localization and mapping (InfoLa-SLAM) in large-scale scenes using single-line lidar. The solution employed two novel designs. The first design was a keyframe selection method based on Fisher information, which reduced the computational cost of the nonlinear optimization for the back-end of SLAM by selecting a relatively small number of keyframes while ensuring the accuracy of mapping. The Fisher information was acquired from the point cloud registration between the current frame and the previous keyframe. The second design was an efficient global descriptor for place recognition, which was achieved by designing a unique graphical feature ID to effectively match the local map with the global one. The results showed that compared with traditional keyframe selection strategies (e.g., based on time, angle, or distance), the proposed method allowed for a 35.16% reduction in the number of keyframes in a warehouse with an area of about 10,000 m². The relocalization module demonstrated a high probability (96%) of correction even under high levels of measurement noise (0.05 m), while the time consumption for relocalization was below 28 ms. The proposed InfoLa-SLAM was also compared with Cartographer under the same dataset. The results showed that InfoLa-SLAM achieved very similar mapping accuracy to Cartographer but excelled in lightweight performance, achieving a 9.11% reduction in the CPU load and a significant 56.67% decrease in the memory consumption. Full article

(This article belongs to the Topic Multi-Sensor Integrated Navigation Systems)

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21 pages, 4087 KiB  

Open AccessArticle

Observability-Constrained Resampling-Free Cubature Kalman Filter for GNSS/INS with Measurement Outliers

by Bingbo Cui, Wu Chen, Duojie Weng, Xinhua Wei, Zeyu Sun, Yan Zhao and Yufei Liu

Remote Sens. 2023, 15(18), 4591; https://doi.org/10.3390/rs15184591 - 18 Sep 2023

Cited by 5 | Viewed by 1473

Abstract

Integrating global navigation satellite systems (GNSSs) with inertial navigation systems (INSs) has been widely recognized as an ideal solution for autonomous vehicle navigation. However, GNSSs suffer from disturbances and signal blocking inevitably, making the performance of GNSS/INSs degraded in the occurrence of measurement [...] Read more.

Integrating global navigation satellite systems (GNSSs) with inertial navigation systems (INSs) has been widely recognized as an ideal solution for autonomous vehicle navigation. However, GNSSs suffer from disturbances and signal blocking inevitably, making the performance of GNSS/INSs degraded in the occurrence of measurement outliers. It has been proven that the sigma points-based Kalman filter (KF) performs better than an extended KF in cases where large prior uncertainty is present in the state estimation of a GNSS/INS. By modifying the sigma points directly, the resampling-free sigma point update framework (SUF) propagates more information excepting Gaussian moments of prescribed accuracy, based on which the resampling-free cubature Kalman filter (RCKF) was developed in our previous work. In order to enhance the adaptivity and robustness of the RCKF, the resampling-free SUF depending on dynamic prediction residue should be improved by suppressing the time-varying measurement outlier. In this paper, a robust observability-constrained RCKF (ROCRCKF) is proposed based on adaptive measurement noise covariance estimation and outlier detection, where the occurrence of measurement outliers is modelled by the Bernoulli variable and estimated with the state simultaneously. Experiments based on car-mounted GNSS/INSs are performed to verify the effectiveness of the ROCRCKF, and result indicates that the proposed algorithm outperforms the RCKF in the presence of measurement outliers, where the heading error and average root mean square error of the position are reduced from 1.96° and 6.38 m to 0.27° and 5.95 m, respectively. The ROCRCKF is robust against the measurement outliers and time-varying model uncertainty, making it suitable for the real-time implementation of GNSS/INSs in GNSS-challenged environments. Full article

(This article belongs to the Topic Multi-Sensor Integrated Navigation Systems)

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23 pages, 9552 KiB  

Open AccessArticle

INS/LIDAR/Stereo SLAM Integration for Precision Navigation in GNSS-Denied Environments

by Nader Abdelaziz and Ahmed El-Rabbany

Sensors 2023, 23(17), 7424; https://doi.org/10.3390/s23177424 - 25 Aug 2023

Cited by 4 | Viewed by 2422

Abstract

Traditionally, navigation systems have relied solely on global navigation satellite system (GNSS)/inertial navigation system (INS) integration. When a temporal loss of GNSS signal lock is encountered, these systems would rely on INS, which can sustain short bursts of outages, albeit drift significantly in [...] Read more.

Traditionally, navigation systems have relied solely on global navigation satellite system (GNSS)/inertial navigation system (INS) integration. When a temporal loss of GNSS signal lock is encountered, these systems would rely on INS, which can sustain short bursts of outages, albeit drift significantly in prolonged outages. In this study, an extended Kalman filter (EKF) is proposed to develop an integrated INS/LiDAR/Stereo simultaneous localization and mapping (SLAM) navigation system. The first update stage of the filter integrates the INS with the LiDAR, after which the resultant navigation solution is integrated with the stereo SLAM solution, which yields the final integrated navigation solution. The system was tested for different driving scenarios in urban and rural environments using the raw Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) dataset in the complete absence of the GNSS signal. In addition, the selected KITTI drives covered low and high driving speeds in feature-rich and feature-poor environments. It is shown that the proposed INS/LiDAR/Stereo SLAM navigation system yielded better position estimations in comparison to using the INS without any assistance from onboard sensors. The accuracy improvement was expressed as a reduction of the root-mean-square error (RMSE) by 83% and 82% in the horizontal and up directions, respectively. In addition, the proposed system outperformed the positioning accuracy of some of the state-of-the-art algorithms. Full article

(This article belongs to the Topic Multi-Sensor Integrated Navigation Systems)

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19 pages, 7782 KiB  

Open AccessArticle

Improved Artificial Potential Field Algorithm Assisted by Multisource Data for AUV Path Planning

by Tianyu Xing, Xiaohao Wang, Kaiyang Ding, Kai Ni and Qian Zhou

Sensors 2023, 23(15), 6680; https://doi.org/10.3390/s23156680 - 26 Jul 2023

Cited by 8 | Viewed by 2426

Abstract

With the development of ocean exploration technology, the exploration of the ocean has become a hot research field involving the use of autonomous underwater vehicles (AUVs). In complex underwater environments, the fast, safe, and smooth arrival of target points is key for AUVs [...] Read more.

With the development of ocean exploration technology, the exploration of the ocean has become a hot research field involving the use of autonomous underwater vehicles (AUVs). In complex underwater environments, the fast, safe, and smooth arrival of target points is key for AUVs to conduct underwater exploration missions. Most path-planning algorithms combine deep reinforcement learning (DRL) and path-planning algorithms to achieve obstacle avoidance and path shortening. In this paper, we propose a method to improve the local minimum in the artificial potential field (APF) to make AUVs out of the local minimum by constructing a traction force. The improved artificial potential field (IAPF) method is combined with DRL for path planning while optimizing the reward function in the DRL algorithm and using the generated path to optimize the future path. By comparing our results with the experimental data of various algorithms, we found that the proposed method has positive effects and advantages in path planning. It is an efficient and safe path-planning method with obvious potential in underwater navigation devices. Full article

(This article belongs to the Topic Multi-Sensor Integrated Navigation Systems)

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24 pages, 25519 KiB  

Open AccessArticle

Research on High Precision Positioning Method for Pedestrians in Indoor Complex Environments Based on UWB/IMU

by Hao Zhang, Qing Wang, Zehui Li, Jing Mi and Kai Zhang

Remote Sens. 2023, 15(14), 3555; https://doi.org/10.3390/rs15143555 - 15 Jul 2023

Cited by 5 | Viewed by 1811

Abstract

Location information is the core data in IoT applications, which is the essential foundation for scene interpretation and interconnection of everything, and thus high-precision positioning is becoming an immediate need. However, the non-line-of-sight (NLOS) effect of indoor complex environment on UWB signal occlusion [...] Read more.

Location information is the core data in IoT applications, which is the essential foundation for scene interpretation and interconnection of everything, and thus high-precision positioning is becoming an immediate need. However, the non-line-of-sight (NLOS) effect of indoor complex environment on UWB signal occlusion has been a major factor limiting the improvement in ultra-wideband (UWB) positioning accuracy, and the optimization of NLOS error has not yet been studied in a targeted manner. To this end, this paper deeply analyzes indoor scenes, divides NLOS into two forms of spatial occlusion and human occlusion, and proposes a particle filtering algorithm based on LOS/NLOS mapping and NLOS error optimization. This algorithm is targeted to optimize the influence of two different forms of NLOS, using spatial a priori information to accurately judge the LOS/NLOS situation of the anchor, optimizing the NLOS anchor ranging using IMU to project the virtual position, judging whether the LOS anchor is affected by human occlusion, and correcting the affected LOS anchor using the established human occlusion error model. Through experimental verification, the algorithm can effectively suppress two different NLOS errors of spatial structure and human occlusion and can achieve continuous and reliable high-precision positioning and tracking in complex indoor environments. Full article

(This article belongs to the Topic Multi-Sensor Integrated Navigation Systems)

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23 pages, 7955 KiB  

Open AccessArticle

Research on Kinematic and Static Filtering of the ESKF Based on INS/GNSS/UWB

by Zongbin Ren, Songlin Liu, Jun Dai, Yunzhu Lv and Yun Fan

Sensors 2023, 23(10), 4735; https://doi.org/10.3390/s23104735 - 14 May 2023

Cited by 3 | Viewed by 2084

Abstract

With the widespread development of multiple sensors for UGVs, the multi-source fusion-navigation system, which overcomes the limitations of the use of a single sensor, is becoming increasingly important in the field of autonomous navigation for UGVs. Because federated filtering is not independent between [...] Read more.

With the widespread development of multiple sensors for UGVs, the multi-source fusion-navigation system, which overcomes the limitations of the use of a single sensor, is becoming increasingly important in the field of autonomous navigation for UGVs. Because federated filtering is not independent between the filter-output quantities, owing to the use of the same state equation in each of the local sensors, a new kinematic and static multi-source fusion-filtering algorithm based on the error-state Kalman filter (ESKF) is proposed in this paper for the positioning-state estimation of UGVs. The algorithm is based on INS/GNSS/UWB multi-source sensors, and the ESKF replaces the traditional Kalman filter in kinematic and static filtering. After constructing the kinematic EKSF based on GNSS/INS and the static ESKF based on UWB/INS, the error-state vector solved by the kinematic ESKF was injected and set to zero. On this basis, the kinematic ESKF filter solution was used as the state vector of the static ESKF for the rest of the static filtering in a sequential form. Finally, the last static ESKF filtering solution was used as the integral filtering solution. Through mathematical simulations and comparative experiments, it is demonstrated that the proposed method converges quickly, and the positioning accuracy of the method was improved by 21.98% and 13.03% compared to the loosely coupled GNSS/INS and the loosely coupled UWB/INS navigation methods, respectively. Furthermore, as shown by the error-variation curves, the main performance of the proposed fusion-filtering method was largely influenced by the accuracy and robustness of the sensors in the kinematic ESKF. Furthermore, the algorithm proposed in this paper demonstrated good generalizability, plug-and-play, and robustness through comparative analysis experiments. Full article

(This article belongs to the Topic Multi-Sensor Integrated Navigation Systems)

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12 pages, 2521 KiB  

Open AccessArticle

Tropospheric Delay Model Based on VMF and ERA5 Reanalysis Data

by Mengtao Zhang, Mengli Wang, Hang Guo, Junjun Hu and Jian Xiong

Appl. Sci. 2023, 13(9), 5789; https://doi.org/10.3390/app13095789 - 8 May 2023

Cited by 2 | Viewed by 1618

Abstract

The global tropospheric zenith delay grid products of VMF1 and VMF3 (Vienna mapping functions) with different resolutions are used to calculate the tropospheric zenith delay of eight IGS (International GNSS Service) stations in China, and the accuracy of the two products under different [...] Read more.

The global tropospheric zenith delay grid products of VMF1 and VMF3 (Vienna mapping functions) with different resolutions are used to calculate the tropospheric zenith delay of eight IGS (International GNSS Service) stations in China, and the accuracy of the two products under different interpolation methods is analyzed. As a result, the accuracy of utilizing different interpolation methods shows no obvious differences. The interpolation accuracy of the VMF3 grid model is slightly higher than that of the VMF1, and the interpolation accuracy of tropospheric delay is related to the elevation difference of grid points. In addition, according to ERA5 (the fifth generation of the Global Climate Information Analysis data set), the atmospheric stratification tropospheric delay is obtained, and a ZTD (the zenith tropospheric delay) height change grid model is constructed using the least squares exponential fitting method. The accuracy of the model is verified using the tropospheric delay product provided by the IGS. Finally, the constructed ZTD height change grid model is used as ZTD height reduction to solve the problem of large tropospheric delay errors in the VMF interpolation when the height change is large. The model accuracy of URUM station improve from 96.47 mm.to 32.97 mm (65.82%). Full article

(This article belongs to the Topic Multi-Sensor Integrated Navigation Systems)

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25 pages, 1401 KiB  

Open AccessReview

A Comprehensive Review of GNSS/INS Integration Techniques for Land and Air Vehicle Applications

by Nurlan Boguspayev, Daulet Akhmedov, Almat Raskaliyev, Alexandr Kim and Anna Sukhenko

Appl. Sci. 2023, 13(8), 4819; https://doi.org/10.3390/app13084819 - 12 Apr 2023

Cited by 29 | Viewed by 7662

Abstract

Navigation systems are of interest for applications in both civilian and military vehicles. Satellite navigation systems and inertial navigation systems are the most applied in this area. They have complementary properties, which has led to a trend of integrating these systems. At present, [...] Read more.

Navigation systems are of interest for applications in both civilian and military vehicles. Satellite navigation systems and inertial navigation systems are the most applied in this area. They have complementary properties, which has led to a trend of integrating these systems. At present, there are several approaches to GNSS/INS integration: loosely coupled, tightly coupled and deeply coupled and many approaches to their modifications in dependence of application and arising problems with measurements, such as lack of GNSS measurements or poor quality of GNSS and INS measurements. This article presents an extensive review of the available modern approaches and their modifications for integrating INS and GNSS measurements, arranging them and highlights the main problems arising for the considered type of integration approach. The article includes a review of various integration tools based on the Kalman filter and intelligent systems, INS mechanization and features of development of an INS measurement error model that is necessary for integration, the main problems of GNSS/INS integration and a comparative description of the solutions proposed by the authors for solving these problems. The findings of this work are useful for further research in the field of inertial and satellite navigation, as well as for engineers involved in the practical implementation of integrated GNSS/INS systems. Full article

(This article belongs to the Topic Multi-Sensor Integrated Navigation Systems)

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19 pages, 13016 KiB  

Open AccessArticle

Cooperative Navigation for Heterogeneous Air-Ground Vehicles Based on Interoperation Strategy

by Chenfa Shi, Zhi Xiong, Mingxing Chen, Rong Wang and Jun Xiong

Remote Sens. 2023, 15(8), 2006; https://doi.org/10.3390/rs15082006 - 10 Apr 2023

Cited by 2 | Viewed by 1852

Abstract

This paper focuses on the cooperative navigation of heterogeneous air-ground vehicle formations in a Global Navigation Satellite System (GNSS) challenged environment and proposes a cooperative navigation method based on motion estimation and a regionally optimal path planning strategy. In air-ground vehicle formations, unmanned [...] Read more.

This paper focuses on the cooperative navigation of heterogeneous air-ground vehicle formations in a Global Navigation Satellite System (GNSS) challenged environment and proposes a cooperative navigation method based on motion estimation and a regionally optimal path planning strategy. In air-ground vehicle formations, unmanned ground vehicles (UGVs) are equipped with low-precision inertial navigation measurement units and wireless range sensors, which interact with unmanned aerial vehicles (UAVs) equipped with high-precision navigation equipment for cooperative measurement information and use the UAVs as aerial benchmarks for cooperative navigation. Firstly, the Interacting Multiple Model (IMM) algorithm is used to predict the next moment’s motion position of the UGVs. Then regional real-time path optimization algorithms are introduced to design the motion position of the high-precision UAVs so as to improve the formation’s configuration and reduce the geometric dilution of precision (GDOP) of the configuration. Simulation results show that the Dynamic Optimal Configuration Cooperative Navigation (DOC-CN) algorithm can reduce the GDOP of heterogeneous air-ground vehicle formations and effectively improve the overall navigation accuracy of the whole formation. The method is suitable for the cooperative navigation environment of heterogeneous air-ground vehicle formations under GNSS-challenged conditions. Full article

(This article belongs to the Topic Multi-Sensor Integrated Navigation Systems)

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15 pages, 3576 KiB  

Open AccessArticle

A Robust INS/USBL/DVL Integrated Navigation Algorithm Using Graph Optimization

by Peijuan Li, Yiting Liu, Tingwu Yan, Shutao Yang and Rui Li

Sensors 2023, 23(2), 916; https://doi.org/10.3390/s23020916 - 12 Jan 2023

Cited by 10 | Viewed by 3759

Abstract

The Autonomous Underwater Vehicle (AUV) is usually equipped with multiple sensors, such as an inertial navigation system (INS), ultra-short baseline system (USBL), and Doppler velocity log (DVL), to achieve autonomous navigation. Multi-source information fusion is the key to realizing high-precision underwater navigation and [...] Read more.

The Autonomous Underwater Vehicle (AUV) is usually equipped with multiple sensors, such as an inertial navigation system (INS), ultra-short baseline system (USBL), and Doppler velocity log (DVL), to achieve autonomous navigation. Multi-source information fusion is the key to realizing high-precision underwater navigation and positioning. To solve the problem, a fusion scheme based on factor graph optimization (FGO) is proposed. Due to multiple iterations and joint optimization of historical data, FGO could usually show a better performance than the traditional Kalman filter. In addition, considering that USBL and DVL are usually heavily influenced by the environment, outliers are often present. A robust integrated navigation algorithm based on a maximum correntropy criterion and FGO scheme is proposed. The proposed algorithm solves the problem of multi-sensor fusion and non-Gaussian noise. Numerical simulations and field tests demonstrate that the proposed FGO scheme shows a better performance and robustness than the traditional Kalman filter. Compared with the traditional Kalman filtering, the positioning accuracy is improved by 5.3%, 9.1%, and 5.1% in the east, north, and height directions. It can realize a more accurate navigation and positioning of underwater multi-sensors. Full article

(This article belongs to the Topic Multi-Sensor Integrated Navigation Systems)

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29 pages, 13192 KiB  

Open AccessArticle

3PCD-TP: A 3D Point Cloud Descriptor for Loop Closure Detection with Twice Projection

by Gang Wang, Xudong Jiang, Wei Zhou, Yu Chen and Hao Zhang

Remote Sens. 2023, 15(1), 82; https://doi.org/10.3390/rs15010082 - 23 Dec 2022

Cited by 5 | Viewed by 2660

Abstract

Loop closure detection (LCD) can effectively eliminate the cumulative errors in simultaneous localization and mapping (SLAM) by detecting the position of a revisit and building interframe pose constraint relations. However, in real-world natural scenes, driverless ground vehicles or robots usually revisit the same [...] Read more.

Loop closure detection (LCD) can effectively eliminate the cumulative errors in simultaneous localization and mapping (SLAM) by detecting the position of a revisit and building interframe pose constraint relations. However, in real-world natural scenes, driverless ground vehicles or robots usually revisit the same place from a different position, meaning that the descriptor cannot give a uniform description of similar scenes, failing LCD. Against this problem, this paper proposes a 3D point cloud descriptor with Twice Projection (3PCD-TP) for the calculation of the similarities between scenes. First, we redefined the origin and primary direction of point clouds according to their distribution and unified their coordinate system, thereby reducing the interference in position recognition due to the rotation and translation of sensors. Next, using the semantic and altitudinal information of point clouds, we generated the 3D descriptor 3PCD-TP with multidimensional features to enhance its ability to describe similar scenes. Following this, we designed a weighting similarity calculation method to reduce the false detection rate of LCD by taking advantage of the property that 3PCD-TP can be projected from multiple angles. Finally, we validated our method using KITTI and the Jilin University (JLU) campus dataset. The experimental results show that our method demonstrated a high level of precision and recall and exhibited greater performance in the face of scenes with reverse loop closure, such as opposite lanes. Full article

(This article belongs to the Topic Multi-Sensor Integrated Navigation Systems)

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13 pages, 3250 KiB  

Open AccessArticle

Automatic Correction of an Automated Guided Vehicle’s Course Using Measurements from a Laser Rangefinder

by Magdalena Dobrzanska and Pawel Dobrzanski

Appl. Sci. 2022, 12(24), 12826; https://doi.org/10.3390/app122412826 - 14 Dec 2022

Cited by 5 | Viewed by 1864

Abstract

In order for AGVs to be able to effectively carry out the tasks assigned to them, it is important to accurately determine their position and orientation in the working space. Having data on the location of an AGV is crucial for the navigation [...] Read more.

In order for AGVs to be able to effectively carry out the tasks assigned to them, it is important to accurately determine their position and orientation in the working space. Having data on the location of an AGV is crucial for the navigation process, and the most commonly used odometry method is unreliable due to errors. To correct these errors, additional measuring systems are used. These systems use a variety of sensors. Some of the most widely used types are laser rangefinders. These sensors are also used in the automatic course correction methodology that is developed and presented in this article. The measurements from laser rangefinders are used to determine the shift of the actual trajectory from the set one, and then to guide the AGV to the previously set course. The developed methodology is experimentally verified on the basis of several dozen test drives. The conducted experimental studies prove the correctness of the developed methodology. The proposed course correction algorithm can be implemented in most working conditions, and guarantees correct passage over the given route. Full article

(This article belongs to the Topic Multi-Sensor Integrated Navigation Systems)

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15 pages, 3800 KiB  

Open AccessArticle

A Novel ML-Aided Methodology for SINS/GPS Integrated Navigation Systems during GPS Outages

by Jin Sun, Zhengyu Chen and Fu Wang

Remote Sens. 2022, 14(23), 5932; https://doi.org/10.3390/rs14235932 - 23 Nov 2022

Cited by 5 | Viewed by 2142

Abstract

To improve the navigation accuracy for land vehicles during global positioning system (GPS) outages, a machine learning (ML) aided methodology to integrate a strap-down inertial navigation system (SINS) and GPS system is proposed, as follows. When a GPS signal is available, an online [...] Read more.

To improve the navigation accuracy for land vehicles during global positioning system (GPS) outages, a machine learning (ML) aided methodology to integrate a strap-down inertial navigation system (SINS) and GPS system is proposed, as follows. When a GPS signal is available, an online sequential extreme learning machine with a dynamic forgetting factor (DOS-ELM) algorithm is used to train the mapping model between the SINS’ acceleration, specific force, speed/position increments outputs, and the GPS’ speed/position increments. When a GPS signal is unavailable, GPS speed/velocity measurements are replaced with prediction output of the well-trained DOS-ELM module’s prediction output, and information fusion with the SINS reduces the degree of system error divergence. A land vehicle field experiment’s actual sensor data were collected online, and the DOS-ELM-aided methodology for the SINS/GPS integrated navigation systems was applied. The simulation results indicate that the proposed methodology can reduce the degree of system error divergence and then obtain accurate and reliable navigation information during GPS outages. Full article

(This article belongs to the Topic Multi-Sensor Integrated Navigation Systems)

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30 pages, 7619 KiB  

Open AccessArticle

SUNS: A User-Friendly Scheme for Seamless and Ubiquitous Navigation Based on an Enhanced Indoor-Outdoor Environmental Awareness Approach

by Ahmed Mansour and Wu Chen

Remote Sens. 2022, 14(20), 5263; https://doi.org/10.3390/rs14205263 - 21 Oct 2022

Cited by 5 | Viewed by 2254

Abstract

Ubiquitous and seamless indoor-outdoor (I/O) localization is the primary objective for gaining more user satisfaction and sustaining the prosperity of the location-based services (LBS) market. Regular users, on the other hand, may be unaware of the impact of activating multiple localization sources on [...] Read more.

Ubiquitous and seamless indoor-outdoor (I/O) localization is the primary objective for gaining more user satisfaction and sustaining the prosperity of the location-based services (LBS) market. Regular users, on the other hand, may be unaware of the impact of activating multiple localization sources on localization performance and energy consumption, or may lack experience deciding when to enable or disable localization sources in different environments. Consequently, an automatic handover mechanism that can handle these decisions on a user’s behalf can appreciably improve user satisfaction. This study introduces an enhanced I/O environmental awareness service that provides an automated handover mechanism for seamless navigation based on multi-sensory navigation integration schemes. Moreover, the proposed service utilizes low-power consumption sensor (LPCS) indicators to execute continuous detection tasks and invoke GNSS in confusion scenarios, and transition intervals to make the most firm decision on the credibility of the LPCS-triggered transition and compensate for indicator thresholds. In this manner, GNSS are used for short intervals that help reduce detection latency and power consumption. Consequently, the proposed service guarantees accurate and reliable I/O detection while preserving low power consumption. Leveraging the proposed service as an automated handover helped realize seamless indoor-outdoor localization with less switching latency, using an integrated solution based on extended Kalman filter. Furthermore, the proposed energy-efficient service was utilized to confine crowdsourced data collection to the required areas (indoors and semi-indoors) and prevent excess data collection outdoors, thereby reducing power drainage. Accordingly, the negative impact of data collection on the user’s device can be mitigated, participation can be encouraged, and crowdsourcing systems can be widely adopted. Full article

(This article belongs to the Topic Multi-Sensor Integrated Navigation Systems)

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13 pages, 5212 KiB  

Open AccessArticle

Semantic Lidar-Inertial SLAM for Dynamic Scenes

by Zean Bu, Changku Sun and Peng Wang

Appl. Sci. 2022, 12(20), 10497; https://doi.org/10.3390/app122010497 - 18 Oct 2022

Cited by 2 | Viewed by 2204

Abstract

Over the past few years, many impressive lidar-inertial SLAM systems have been developed and perform well under static scenes. However, most tasks are under dynamic environments in real life, and the determination of a method to improve accuracy and robustness poses a challenge. [...] Read more.

Over the past few years, many impressive lidar-inertial SLAM systems have been developed and perform well under static scenes. However, most tasks are under dynamic environments in real life, and the determination of a method to improve accuracy and robustness poses a challenge. In this paper, we propose a semantic lidar-inertial SLAM approach with the combination of a point cloud semantic segmentation network and lidar-inertial SLAM LIO mapping for dynamic scenes. We import an attention mechanism to the PointConv network to build an attention weight function to improve the capacity to predict details. The semantic segmentation results of the point clouds from lidar enable us to obtain point-wise labels for each lidar frame. After filtering the dynamic objects, the refined global map of the lidar-inertial SLAM sytem is clearer, and the estimated trajectory can achieve a higher precision. We conduct experiments on an UrbanNav dataset, whose challenging highway sequences have a large number of moving cars and pedestrians. The results demonstrate that, compared with other SLAM systems, the accuracy of trajectory can be improved to different degrees. Full article

(This article belongs to the Topic Multi-Sensor Integrated Navigation Systems)

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18 pages, 5932 KiB  

Open AccessArticle

Semi-Direct Point-Line Visual Inertial Odometry for MAVs

by Bo Gao, Baowang Lian and Chengkai Tang

Appl. Sci. 2022, 12(18), 9265; https://doi.org/10.3390/app12189265 - 15 Sep 2022

Viewed by 1840

Abstract

Traditional Micro-Aerial Vehicles (MAVs) are usually equipped with a low-cost Inertial Measurement Unit (IMU) and monocular cameras, how to achieve high precision and high reliability navigation under the framework of low computational complexity is the main problem for MAVs. To this end, a [...] Read more.

Traditional Micro-Aerial Vehicles (MAVs) are usually equipped with a low-cost Inertial Measurement Unit (IMU) and monocular cameras, how to achieve high precision and high reliability navigation under the framework of low computational complexity is the main problem for MAVs. To this end, a novel semi-direct point-line visual inertial odometry (SDPL-VIO) has been proposed for MAVs. In the front-end, point and line features are introduced to enhance image constraints and increase environmental adaptability. At the same time, the semi-direct method combined with IMU pre-integration is used to complete motion estimation. This hybrid strategy combines the accuracy and loop closure detection performance of the feature-based method with the rapidity of the direct method, and tracks keyframes and non-keyframes, respectively. In the back-end, the sliding window mechanism is adopted to limit the computation, while the improved marginalization method is used to decompose the high-dimensional matrix corresponding to the cost function to reduce the computational complexity in the optimization process. The comparison results in the EuRoC datasets demonstrate that SDPL-VIO performs better than the other state-of-the-art visual inertial odometry (VIO) methods, especially in terms of accuracy and real-time performance. Full article

(This article belongs to the Topic Multi-Sensor Integrated Navigation Systems)

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22 pages, 6323 KiB  

Open AccessArticle

Research on a Wi-Fi RSSI Calibration Algorithm Based on WOA-BPNN for Indoor Positioning

by Min Yu, Shuyin Yao, Xuan Wu and Liang Chen

Appl. Sci. 2022, 12(14), 7151; https://doi.org/10.3390/app12147151 - 15 Jul 2022

Cited by 5 | Viewed by 2184

Abstract

Owing to the heterogeneity of software and hardware in different types of mobile terminals, the received signal strength indication (RSSI) from the same Wi-Fi access point (AP) varies in indoor environments, which can affect the positioning accuracy of fingerprint methods. To solve this [...] Read more.

Owing to the heterogeneity of software and hardware in different types of mobile terminals, the received signal strength indication (RSSI) from the same Wi-Fi access point (AP) varies in indoor environments, which can affect the positioning accuracy of fingerprint methods. To solve this problem and consider the nonlinear characteristics of Wi-Fi signal strength propagation and attenuation, we propose a whale optimisation algorithm-back-propagation neural network (WOA-BPNN) model for indoor Wi-Fi RSSI calibration. Firstly, as the selection of the initial parameters of the BPNN model has a considerable impact on the positioning accuracy of the calibration algorithm, we use the WOA to avoid blindly selecting the parameters of the BPNN model. Then, we propose an improved nonlinear convergence factor to balance the searchability of the WOA, which can also help to optimise the calibration algorithm. Moreover, we change the structure of the BPNN model to compare its influence on the calibration effect of the WOA-BPNN calibration algorithm. Secondly, in view of the low positioning accuracy of indoor fingerprint positioning algorithms, we propose a region-adaptive weighted K-nearest neighbour positioning algorithm based on hierarchical clustering. Finally, we effectively combine the two proposed algorithms and compare the results with those of other calibration algorithms such as the linear regression (LR), support vector regression (SVR), BPNN, and genetic algorithm-BPNN (GA-BPNN) calibration algorithms. The test results show that among different mobile terminals, the proposed WOA-BPNN calibration algorithm can increase positioning accuracy (one sigma error) by 41%, 42%, 44% and 36%, on average. The indoor field tests suggest that the proposed methods can effectively reduce the indoor positioning error caused by the heterogeneous differences of software and hardware in different mobile terminals. Full article

(This article belongs to the Topic Multi-Sensor Integrated Navigation Systems)

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16 pages, 5266 KiB  

Open AccessArticle

Autonomous Exploration of Unknown Indoor Environments for High-Quality Mapping Using Feature-Based RGB-D SLAM

by Amr Eldemiry, Yajing Zou, Yaxin Li, Chih-Yung Wen and Wu Chen

Sensors 2022, 22(14), 5117; https://doi.org/10.3390/s22145117 - 7 Jul 2022

Cited by 8 | Viewed by 3166

Abstract

Simultaneous localization and mapping (SLAM) system-based indoor mapping using autonomous mobile robots in unknown environments is crucial for many applications, such as rescue scenarios, utility tunnel monitoring, and indoor 3D modeling. Researchers have proposed various strategies to obtain full coverage while minimizing exploration [...] Read more.

Simultaneous localization and mapping (SLAM) system-based indoor mapping using autonomous mobile robots in unknown environments is crucial for many applications, such as rescue scenarios, utility tunnel monitoring, and indoor 3D modeling. Researchers have proposed various strategies to obtain full coverage while minimizing exploration time; however, mapping quality factors have not been considered. In fact, mapping quality plays a pivotal role in 3D modeling, especially when using low-cost sensors in challenging indoor scenarios. This study proposes a novel exploration algorithm to simultaneously optimize exploration time and mapping quality using a low-cost RGB-D camera. Feature-based RGB-D SLAM is utilized due to its various advantages, such as low computational cost and dense real-time reconstruction ability. Subsequently, our novel exploration strategies consider the mapping quality factors of the RGB-D SLAM system. Exploration time optimization factors are also considered to set a new optimum goal. Furthermore, a Voronoi path planner is adopted for reliable, maximal obstacle clearance and fixed paths. According to the texture level, three exploration strategies are evaluated in three real-world environments. We achieve a significant enhancement in mapping quality and exploration time using our proposed exploration strategies compared to the baseline frontier-based exploration, particularly in a low-texture environment. Full article

(This article belongs to the Topic Multi-Sensor Integrated Navigation Systems)

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15 pages, 5099 KiB  

Open AccessArticle

In-Motion Alignment Method of SINS Based on Improved Kalman Filter under Geographic Latitude Uncertainty

by Jin Sun, Qianqi Ye and Yue Lei

Remote Sens. 2022, 14(11), 2581; https://doi.org/10.3390/rs14112581 - 27 May 2022

Cited by 3 | Viewed by 2020

Abstract

To realize the in-motion alignment of the strapdown inertial navigation system (SINS) under the geographic latitude uncertainty, we propose a latitude estimation and in-motion alignment method based on the integral dynamic window and polynomial fitting (IDW-PF) and improved Kalman filter (IKF). First, the [...] Read more.

To realize the in-motion alignment of the strapdown inertial navigation system (SINS) under the geographic latitude uncertainty, we propose a latitude estimation and in-motion alignment method based on the integral dynamic window and polynomial fitting (IDW-PF) and improved Kalman filter (IKF). First, the integral dynamic window (IDW) is designed to smooth out the high-frequency line motion interference and accelerometer noise. Second, the specific force integral is performed for a cubic polynomial fitting (PF) with time as an independent variable to further suppress the line motion interference. Simultaneously, the latitude is estimated according to the geometric relationship between the angle of the gravitational acceleration vectors at different moments and the latitude. Finally, the IKF based on the multi-fading factor is designed for the in-motion alignment of SINS. A simulation experiment is conducted to verify the proposed latitude estimation and in-motion alignment method. The results indicate that the latitude can be estimated well by the method based on the IDW-PF; the mean and standard deviation of the estimated latitude can achieve −0.016° and 0.013° within 300 s. The trapezoidal maneuvering path is optimal when IKF is used, the pitch error is 0.0002°, the roll error is 0.0009° and the heading error is −0.0047° after the alignment ends at 900 s. Full article

(This article belongs to the Topic Multi-Sensor Integrated Navigation Systems)

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11 pages, 3981 KiB  

Open AccessArticle

Inversion Method of Tidal Level Based on GNSS Triple-Frequency, Geometry-Free, Non-Ionospheric Phase Combination

by Gaochong You, Hang Guo, Jianfeng Wu and Min Yu

Appl. Sci. 2022, 12(10), 4983; https://doi.org/10.3390/app12104983 - 14 May 2022

Viewed by 2169

Abstract

Using the navigation signal transmitted by GNSS (global navigation satellite system), satellites for tide level monitoring comprise one of the important research fields of GNSS marine remote sensing. Regarding the problem that GNSS-MR (multipath reflectometry) technology only uses carrier SNR (signal noise ratio) [...] Read more.

Using the navigation signal transmitted by GNSS (global navigation satellite system), satellites for tide level monitoring comprise one of the important research fields of GNSS marine remote sensing. Regarding the problem that GNSS-MR (multipath reflectometry) technology only uses carrier SNR (signal noise ratio) data, resulting in the lack of SNR data for early CORS (continuously operating reference stations) stations, it is impossible to carry out tide level inversion. In this paper, a method of tide level inversion based on triple-frequency geometric ionospheric free combined-phase observations instead of SNR is proposed. The simultaneous interpretation of GNSS satellite observations from the sc02 station in Friday Harbor in the US is carried out and compared with the traditional GNSS-IR (interference and reflectometry) tide-inversion method. The experimental results show that the tide level inversion method proposed in this paper has the same tide level trend as the measured tide level trend. The accuracy evaluation shows that the RMSE value of tide level inversion is 15 cm and the correlation coefficient r is 0.984, which verifies the effectiveness of this method for tide level monitoring and expands the method of GNSS tide-level monitoring. Full article

(This article belongs to the Topic Multi-Sensor Integrated Navigation Systems)

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