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23 pages, 5997 KiB

Open AccessArticle

An Improved Pedestrian Navigation Method Based on the Combination of Indoor Map Assistance and Adaptive Particle Filter

by Zhengchun Wang, Li Xing, Zhi Xiong, Yiming Ding, Yinshou Sun and Chenfa Shi

Remote Sens. 2022, 14(24), 6282; https://doi.org/10.3390/rs14246282 - 11 Dec 2022

Cited by 3 | Viewed by 1995

Abstract

At present, the traditional indoor pedestrian navigation methods mainly include pedestrian dead reckoning (PDR) and zero velocity update (ZUPT), but these methods have the problem of error divergence during long time navigation. To solve this problem, under the condition of not relying on [...] Read more.

At present, the traditional indoor pedestrian navigation methods mainly include pedestrian dead reckoning (PDR) and zero velocity update (ZUPT), but these methods have the problem of error divergence during long time navigation. To solve this problem, under the condition of not relying on the active sensing information, combined with the characteristics of particles “not going through the wall” in the indoor map building structure, an improved adaptive particle filter (PF) based on the particle “not going through the wall” method is proposed for pedestrian navigation in this paper. This method can restrain the error divergence of the navigation system for a long time. Compared to the traditional pedestrian navigation method, based on the combination of indoor map assistance (MA) and particle filter, a global search method based on indoor MA is used to solve the indoor positioning problem under the condition of the unknown initial position and heading. In order to solve the problem of low operation efficiency caused by the large number of particles in PF, a calculation method of adaptively adjusting the number of particles in the process of particle resampling is proposed. The results of the simulation data and actual test data show that the proposed indoor integrated positioning method can effectively suppress the error divergence problem of the navigation system. Under the condition that the total distance is more than 415.44 m in the indoor environment of about 2600 m², the average error and the maximum error of the position are less than two meters relative to the reference point. Full article

(This article belongs to the Special Issue Advanced Technologies for Position and Navigation under GNSS Signal Challenging or Denied Environments)

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18 pages, 4081 KiB

Open AccessArticle

An Aerial and Ground Multi-Agent Cooperative Location Framework in GNSS-Challenged Environments

by Haoyuan Xu, Chaochen Wang, Yuming Bo, Changhui Jiang, Yanxi Liu, Shijie Yang and Weisong Lai

Remote Sens. 2022, 14(19), 5055; https://doi.org/10.3390/rs14195055 - 10 Oct 2022

Cited by 3 | Viewed by 3100

Abstract

In order to realize the cooperative localization of multi-unmanned platforms in the GNSS-denied environment, this paper proposes a collaborative SLAM (simultaneous localization and mapping, SLAM) framework based on image feature point matching. Without GNSS, a single unmanned platform UGV and UAV (unmanned ground [...] Read more.

In order to realize the cooperative localization of multi-unmanned platforms in the GNSS-denied environment, this paper proposes a collaborative SLAM (simultaneous localization and mapping, SLAM) framework based on image feature point matching. Without GNSS, a single unmanned platform UGV and UAV (unmanned ground vehicle, UGV; unmanned aerial vehicle, UAV) equipped with vision and IMU (inertial measurement unit, IMU) sensors can exchange information through data communication to jointly build a three-dimensional visual point map, and determine the relative position of each other through visual-based position re- identification and PnP (Perspective-n-Points, PnP) methods. When any agent can receive reliable GNSS signals, GNSS positioning information will greatly improve the positioning accuracy without changing the positioning algorithm framework. In order to achieve this function, we designed a set of two-stage position estimation algorithms. In the first stage, we used the modified ORB-SLAM3 algorithm for position estimation by fusing visual and IMU information. In the second stage, we integrated GNSS positioning and cooperative positioning information using the factor graph optimization (FGO) algorithm. Our framework consists of an UGV as the central server node and three UAVs carried by the UGV, that will collaborate on space exploration missions. Finally, we simulated the influence of different visibility and lighting conditions on the framework function on the virtual simulation experiment platform built based on ROS (robot operating system, ROS) and Unity3D. The accuracy of the cooperative localization algorithm and the single platform localization algorithm was evaluated. In the two cases of GNSS-denied and GNSS-challenged, the error of co-location reduced by 15.5% and 19.7%, respectively, compared with single-platform independent positioning. Full article

(This article belongs to the Special Issue Advanced Technologies for Position and Navigation under GNSS Signal Challenging or Denied Environments)

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28 pages, 25960 KiB

Open AccessArticle

3D LiDAR Aided GNSS/INS Integration Fault Detection, Localization and Integrity Assessment in Urban Canyons

by Zhipeng Wang, Bo Li, Zhiqiang Dan, Hongxia Wang and Kun Fang

Remote Sens. 2022, 14(18), 4641; https://doi.org/10.3390/rs14184641 - 16 Sep 2022

Cited by 9 | Viewed by 3000

Abstract

The performance of Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS) integrated navigation can be severely degraded in urban canyons due to the non-line-of-sight (NLOS) signals and multipath effects. Therefore, to achieve a high-precision and robust integrated system, real-time fault detection [...] Read more.

The performance of Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS) integrated navigation can be severely degraded in urban canyons due to the non-line-of-sight (NLOS) signals and multipath effects. Therefore, to achieve a high-precision and robust integrated system, real-time fault detection and localization algorithms are needed to ensure integrity. Currently, the residual chi-square test is used for fault detection in the positioning domain, but it has poor sensitivity when faults disappear. Three-dimensional (3D) light detection and ranging (LiDAR) has good positioning performance in complex environments. First, a LiDAR aided real-time fault detection algorithm is proposed. A test statistic is constructed by the mean deviation of the matched targets, and a dynamic threshold is constructed by a sliding window. Second, to solve the problem that measurement noise is estimated by prior modeling with a certain error, a LiDAR aided real-time measurement noise estimation based on adaptive filter localization algorithm is proposed according to the position deviations of matched targets. Finally, the integrity of the integrated system is assessed. The error bound of integrated positioning is innovatively verified with real test data. We conduct two experiments with a vehicle going through a viaduct and a floor hole, which, represent mid and deep urban canyons, respectively. The experimental results show that in terms of fault detection, the fault could be detected in mid urban canyons and the response time of fault disappearance is reduced by 70.24% in deep urban canyons. Thus, the poor sensitivity of the residual chi-square test for fault disappearance is improved. In terms of localization, the proposed algorithm is compared with the optimal fading factor adaptive filter (OFFAF) and the extended Kalman filter (EKF). The proposed algorithm is the most effective, and the Root Mean Square Error (RMSE) in the east and north is reduced by 12.98% and 35.1% in deep urban canyons. Regarding integrity assessment, the error bound can overbound the positioning errors in deep urban canyons relative to the EKF and the mean value of the error bounds is reduced. Full article

(This article belongs to the Special Issue Advanced Technologies for Position and Navigation under GNSS Signal Challenging or Denied Environments)

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19 pages, 7691 KiB

Open AccessArticle

A Robust Nonlinear Filter Strategy Based on Maximum Correntropy Criterion for Multi-GNSS and Dual-Frequency RTK

by Jian Liu, Tong Liu, Yuanfa Ji, Mengfei Sun, Mingyang Lyu, Bing Xu, Zhiping Lu and Guochang Xu

Remote Sens. 2022, 14(18), 4578; https://doi.org/10.3390/rs14184578 - 13 Sep 2022

Cited by 1 | Viewed by 1985

Abstract

The multi-constellation, multi-frequency Global Navigation Satellite System (GNSS) has the potential to empower precise real-time kinematics (RTK) with higher accuracy, availability, continuity, and integrity. However, to enhance the robustness of the nonlinear filter, both the measurement quality and efficiency of parameter estimation need [...] Read more.

The multi-constellation, multi-frequency Global Navigation Satellite System (GNSS) has the potential to empower precise real-time kinematics (RTK) with higher accuracy, availability, continuity, and integrity. However, to enhance the robustness of the nonlinear filter, both the measurement quality and efficiency of parameter estimation need consideration, especially for GNSS challenging or denied environments where outliers and non-Gaussian noise exist. This study proposes a nonlinear Kalman filter with adaptive kernel bandwidth (KBW) based on the maximum correntropy criterion (AMC-KF). The proposed method excavates data features of higher order moments to enhance the robustness against noise. With the wide-lane and ionosphere-free combination, a dual frequency (DF) data-aided ambiguity resolution (AR) method is also derived to improve the measurement quality. The filtering strategy based on the DF data-aided AR method and AMC-KF is applied for multi-GNSS and DF RTK. To evaluate the proposed method, the short baseline test, long baseline test, and triangle network closure test are conducted with DF data from GPS and Galileo. For the short baseline test, the proposed filter strategy could improve the positioning accuracy by more than 30% on E and N components, and 60% on U. The superiority of the proposed adaptive KBW is validated both in efficiency and accuracy. The triangle network closure test shows that the proposed DF data-aided AR method could achieve a success rate of more than 93%. For the long baseline test, the integration of the above methods gains more than 40% positioning accuracy improvement on ENU components. This study shows that the proposed nonlinear strategy could enhance both robustness and accuracy without the assistance of external sensors and is applicable for multi-GNSS and dual-frequency RTK. Full article

(This article belongs to the Special Issue Advanced Technologies for Position and Navigation under GNSS Signal Challenging or Denied Environments)

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17 pages, 7306 KiB

Open AccessArticle

A Novel Method for AI-Assisted INS/GNSS Navigation System Based on CNN-GRU and CKF during GNSS Outage

by Shuai Zhao, Yilan Zhou and Tengchao Huang

Remote Sens. 2022, 14(18), 4494; https://doi.org/10.3390/rs14184494 - 9 Sep 2022

Cited by 27 | Viewed by 4187

Abstract

In the fields of positioning and navigation, the integrated inertial navigation system (INS)/global navigation satellite systems (GNSS) are frequently employed. Currently, high-precision INS typically utilizes fiber optic gyroscopes (FOGs) and quartz flexural accelerometers (QFAs) rather than MEMS sensors. But when GNSS signals are [...] Read more.

In the fields of positioning and navigation, the integrated inertial navigation system (INS)/global navigation satellite systems (GNSS) are frequently employed. Currently, high-precision INS typically utilizes fiber optic gyroscopes (FOGs) and quartz flexural accelerometers (QFAs) rather than MEMS sensors. But when GNSS signals are not available, the errors of high-precision INS also disperse rapidly, similar to MEMS-INS when GNSS signals would be unavailable for a long time, leading to a serious degradation of the navigation accuracy. This paper presents a new AI-assisted method for the integrated high-precision INS/GNSS navigation system. The position increments during GNSS outage are predicted by the convolutional neural network-gated recurrent unit (CNN-GRU). In the process, the CNN is utilized to quickly extract the multi-dimensional sequence features, and GRU is used to model the time series. In addition, a new real-time training strategy is proposed for practical application scenarios, where the duration of the GNSS outage time and the motion state information of the vehicle are taken into account in the training strategy. The real road test results verify that the proposed algorithm has the advantages of high prediction accuracy and high training efficiency. Full article

(This article belongs to the Special Issue Advanced Technologies for Position and Navigation under GNSS Signal Challenging or Denied Environments)

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23 pages, 8039 KiB

Open AccessArticle

LIDAR-Inertial Real-Time State Estimator with Rod-Shaped and Planar Feature

by Hong Liu, Shuguo Pan, Wang Gao, Chun Ma, Fengshuo Jia and Xinyu Lu

Remote Sens. 2022, 14(16), 4031; https://doi.org/10.3390/rs14164031 - 18 Aug 2022

Viewed by 2331

Abstract

State estimation and mapping based on Light Detection and Ranging (LIDAR) are important for autonomous systems. Point cloud registration is a crucial module affecting the accuracy and real-time performance of LIDAR simultaneous localization and mapping (SLAM). In this paper, a novel point cloud [...] Read more.

State estimation and mapping based on Light Detection and Ranging (LIDAR) are important for autonomous systems. Point cloud registration is a crucial module affecting the accuracy and real-time performance of LIDAR simultaneous localization and mapping (SLAM). In this paper, a novel point cloud feature selection for LIDAR-inertial tightly coupled systems is proposed. In the front-end, a point cloud registration is carried out after marking rod-shaped and planar feature information which is different from the existing LIDAR and inertial measurement unit (IMU) integration scheme. This preprocessing method subsequently reduces the outliers. IMU pre-integration outputs high-frequency result and is used to provide the initial value for LIDAR solution. In the scan-to-map module, a computationally efficient graph optimization framework is applied. Moreover, the LIDAR odometry further constrains the IMU states. In the back-end, the optimization based on sliding-window incorporates the LIDAR-inertial measurement and loop closure global constraints to reduce the cumulative error. Combining the front-end and back-end, we propose the low drift and high real-time LIDAR-inertial positioning system. Furthermore, we conducted an exhaustive comparison in open data sequences and real-word experiments. The proposed system outperforms much higher positioning accuracy than the state-of-the-art methods in various scenarios. Compared with the LIO-SAM, the absolute trajectory error (ATE) average RMSE (Root Mean Square Error) in this study increases by 64.45% in M2DGR street dataset (street_01, 04, 07, 10) and 24.85% in our actual scene datasets. In the most time-consuming mapping module of each system, our system runtime can also be significantly reduced due to the front-end preprocessing and back-end graph model. Full article

(This article belongs to the Special Issue Advanced Technologies for Position and Navigation under GNSS Signal Challenging or Denied Environments)

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31 pages, 52074 KiB

Open AccessArticle

FPS: Fast Path Planner Algorithm Based on Sparse Visibility Graph and Bidirectional Breadth-First Search

by Qunzhao Li, Fei Xie, Jing Zhao, Bing Xu, Jiquan Yang, Xixiang Liu and Hongbo Suo

Remote Sens. 2022, 14(15), 3720; https://doi.org/10.3390/rs14153720 - 3 Aug 2022

Cited by 19 | Viewed by 4021

Abstract

The majority of planning algorithms used are based on the occupancy grid maps, but in complicated situations, the occupancy grid maps have a significant search overhead. This paper proposed a path planner based on the visibility graph (v-graph) for the mobile robot that [...] Read more.

The majority of planning algorithms used are based on the occupancy grid maps, but in complicated situations, the occupancy grid maps have a significant search overhead. This paper proposed a path planner based on the visibility graph (v-graph) for the mobile robot that uses sparse methods to speed up and simplify the construction of the v-graph. Firstly, the complementary grid framework is designed to reduce graph updating iteration costs during the data collection process in each data frame. Secondly, a filter approach based on the edge length and the number of vertices of the obstacle contour is proposed to reduce redundant nodes and edges in the v-graph. Thirdly, a bidirectional breadth-first search is combined into the path searching process in the proposed fast path planner algorithm in order to reduce the waste of exploring space. Finally, the simulation results indicate that the proposed sparse v-graph planner can significantly improve the efficiency of building the v-graph and reduce the time of path search. In highly convoluted unknown or partially known environments, our method is 40% faster than the FAR Planner and produces paths 25% shorter than it. Moreover, the physical experiment shows that the proposed path planner is faster than the FAR Planner in both the v-graph update process and laser process. The method proposed in this paper performs faster when seeking paths than the conventional method based on the occupancy grid. Full article

(This article belongs to the Special Issue Advanced Technologies for Position and Navigation under GNSS Signal Challenging or Denied Environments)

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17 pages, 8705 KiB

Open AccessArticle

Smartphone-Based Unconstrained Step Detection Fusing a Variable Sliding Window and an Adaptive Threshold

by Ying Xu, Guofeng Li, Zeyu Li, Hao Yu, Jianhui Cui, Jin Wang and Yu Chen

Remote Sens. 2022, 14(12), 2926; https://doi.org/10.3390/rs14122926 - 19 Jun 2022

Cited by 4 | Viewed by 2856

Abstract

Step detection for smartphones plays an important role in the pedestrian dead reckoning (PDR) for indoor positioning. Aiming at the problem of low step detection accuracy of smartphones in complex unconstrained states in PDR, smartphone-based unconstrained step detection method fusing a variable sliding [...] Read more.

Step detection for smartphones plays an important role in the pedestrian dead reckoning (PDR) for indoor positioning. Aiming at the problem of low step detection accuracy of smartphones in complex unconstrained states in PDR, smartphone-based unconstrained step detection method fusing a variable sliding window and an adaptive threshold is proposed. In this method, the dynamic updating algorithm of a peak threshold is developed, and the minimum peak value filtered after a sliding window filter is used as the adaptive peak threshold, which solves the problem that the peak threshold of different motion states is difficult to update adaptively. Then, a variable sliding window collaborative time threshold method is proposed, which solves the problem that the adjacent windows cannot be contacted, and the initial peak and the end peak are difficult to accurately identify. To evaluate the performance of the proposed unconstrained step detection algorithm, 50 experiments in constrained and unconstrained states are conducted by 25 volunteers holding 21 different types of smartphones. Experimental results show: The average step counting accuracy of the proposed unconstrained step detection algorithm is over 98%. Compared with the open source program Stepcount, the average step counting accuracy of the proposed algorithm is improved by 10.0%. The smartphone-based unconstrained step detection fusing a variable sliding window and an adaptive threshold has a strong ability to adapt to complex unconstrained states, and the average step counting accuracy rate is only 0.6% lower than that of constrained states. This algorithm has a wide audience and is friendly for different genders and smartphones with different prices. Full article

(This article belongs to the Special Issue Advanced Technologies for Position and Navigation under GNSS Signal Challenging or Denied Environments)

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18 pages, 6746 KiB

Open AccessArticle

Evaluation of Forest Features Determining GNSS Positioning Accuracy of a Novel Low-Cost, Mobile RTK System Using LiDAR and TreeNet

by Omid Abdi, Jori Uusitalo, Julius Pietarinen and Antti Lajunen

Remote Sens. 2022, 14(12), 2856; https://doi.org/10.3390/rs14122856 - 15 Jun 2022

Cited by 14 | Viewed by 4361

Abstract

Accurate positioning is one of the main components and challenges for precision forestry. This study was established to test the feasibility of a low-cost GNSS receiver, u-blox ZED-F9P, in movable RTK mode with features that determine its positioning accuracy following logging trails in [...] Read more.

Accurate positioning is one of the main components and challenges for precision forestry. This study was established to test the feasibility of a low-cost GNSS receiver, u-blox ZED-F9P, in movable RTK mode with features that determine its positioning accuracy following logging trails in the forest environment. The accuracy of the low-cost receiver was controlled via a geodetic-grade receiver and high-density LiDAR data. The features of nearby logging trails were extracted from the LiDAR data in three main categories: tree characteristics; ground-surface conditions; and crown-surface conditions. An object-based TreeNet approach was used to explore the influential features of the receiver’s positioning accuracy. The results of the TreeNet model indicated that tree height, ground elevation, aspect, canopy-surface elevation, and tree density were the top influencing features. The partial dependence plots showed that tree height above 14 m, ground elevation above 134 m, western direction, canopy-surface elevation above 138 m, and tree density above 30% significantly increased positioning errors by the low-cost receiver over southern Finland. Overall, the low-cost receiver showed high performance in acquiring reliable and consistent positions, when integrated with LiDAR data. The system has a strong potential for navigating machinery in the pathway of precision harvesting in commercial forests. Full article

(This article belongs to the Special Issue Advanced Technologies for Position and Navigation under GNSS Signal Challenging or Denied Environments)

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17 pages, 4140 KiB

Open AccessArticle

A Dual w-Test Based Quality Control Algorithm for Integrated IMU/GNSS Navigation in Urban Areas

by Rui Sun, Ming Qiu, Fei Liu, Zhi Wang and Washington Yotto Ochieng

Remote Sens. 2022, 14(9), 2132; https://doi.org/10.3390/rs14092132 - 29 Apr 2022

Cited by 8 | Viewed by 2561

Abstract

Integration of the Global Navigation Satellite System (GNSS), with Inertial Measurement Unit (IMU) sensors to improve navigation performance, is widely used in many land-based applications. However, further application, especially in urban areas, is limited by the quality (due mainly to multipath effects) and [...] Read more.

Integration of the Global Navigation Satellite System (GNSS), with Inertial Measurement Unit (IMU) sensors to improve navigation performance, is widely used in many land-based applications. However, further application, especially in urban areas, is limited by the quality (due mainly to multipath effects) and availability of GNSS measurements, with a significant impact on performance, especially from low grade integration. To maximize the potential of GNSS measurements, this paper proposes a dual w-test-based quality control algorithm for integrated IMU/GNSS navigation in urban areas. Quality control is achieved through fault detection and exclusion (FDE) with the capability to detect simultaneous multiple faults in measurements from different satellites. The remaining fault-free GNSS measurements are fused with IMU sensor measurements to obtain the final improved state solution. The effectiveness of the algorithm is validated in a deep urban field test. Compared to the cases without fault exclusion, the results show improvements of about 24% and 30% in horizontal and vertical positioning components, respectively. Full article

(This article belongs to the Special Issue Advanced Technologies for Position and Navigation under GNSS Signal Challenging or Denied Environments)

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25 pages, 10591 KiB

Open AccessArticle

A Robot Pose Estimation Optimized Visual SLAM Algorithm Based on CO-HDC Instance Segmentation Network for Dynamic Scenes

by Jinjie Chen, Fei Xie, Lei Huang, Jiquan Yang, Xixiang Liu and Jianjun Shi

Remote Sens. 2022, 14(9), 2114; https://doi.org/10.3390/rs14092114 - 28 Apr 2022

Cited by 10 | Viewed by 3439

Abstract

In order to improve the accuracy of visual SLAM algorithms in a dynamic scene, instance segmentation is widely used to eliminate dynamic feature points. However, the existing segmentation technology has low accuracy, especially for the contour of the object, and the amount of [...] Read more.

In order to improve the accuracy of visual SLAM algorithms in a dynamic scene, instance segmentation is widely used to eliminate dynamic feature points. However, the existing segmentation technology has low accuracy, especially for the contour of the object, and the amount of calculation of instance segmentation is large, limiting the speed of visual SLAM based on instance segmentation. Therefore, this paper proposes a contour optimization hybrid dilated convolutional neural network (CO-HDC) algorithm, which can perform a lightweight calculation on the basis of improving the accuracy of contour segmentation. Firstly, a hybrid dilated convolutional neural network (HDC) is used to increase the receptive field, which is defined as the size of the region in the input that produces the feature. Secondly, the contour quality evaluation (CQE) algorithm is proposed to enhance the contour, retaining the highest quality contour and solving the problem of distinguishing dynamic feature points from static feature points at the contour. Finally, in order to match the mapping speed of visual SLAM, the Beetle Antennae Search Douglas–Peucker (BAS-DP) algorithm is proposed to lighten the contour extraction. The experimental results have demonstrated that the proposed visual SLAM based on the CO-HDC algorithm performs well in the field of pose estimation and map construction on the TUM dataset. Compared with ORB-SLAM2, the Root Mean Squared Error (Rmse) of the proposed method in absolute trajectory error is about 30 times smaller and is only 0.02 m. Full article

(This article belongs to the Special Issue Advanced Technologies for Position and Navigation under GNSS Signal Challenging or Denied Environments)

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26 pages, 6021 KiB

Open AccessArticle

LiDAR-Inertial-GNSS Fusion Positioning System in Urban Environment: Local Accurate Registration and Global Drift-Free

by Xuan He, Shuguo Pan, Wang Gao and Xinyu Lu

Remote Sens. 2022, 14(9), 2104; https://doi.org/10.3390/rs14092104 - 27 Apr 2022

Cited by 12 | Viewed by 3032

Abstract

Aiming at the insufficient accuracy and accumulated error of the point cloud registration of LiDAR-inertial odometry (LIO) in an urban environment, we propose a LiDAR-inertial-GNSS fusion positioning algorithm based on voxelized accurate registration. Firstly, a voxelized point cloud downsampling method based on curvature [...] Read more.

Aiming at the insufficient accuracy and accumulated error of the point cloud registration of LiDAR-inertial odometry (LIO) in an urban environment, we propose a LiDAR-inertial-GNSS fusion positioning algorithm based on voxelized accurate registration. Firstly, a voxelized point cloud downsampling method based on curvature segmentation is proposed. Rough classification is carried out by the curvature threshold, and the voxelized point cloud downsampling is performed using HashMap instead of a random sample consensus algorithm. Secondly, a point cloud registration model based on the nearest neighbors of the point and neighborhood point sets is constructed. Furthermore, an iterative termination threshold is set to reduce the probability of the local optimal solution. The registration time of a single frame point cloud is increased by an order of magnitude. Finally, we propose a LIO-GNSS fusion positioning model based on graph optimization that uses GNSS observations weighted by confidence to globally correct local drift. The experimental results show that the average root mean square error of the absolute trajectory error of our algorithm is 1.58m on average in a large-scale outdoor environment, which is approximately 83.5% higher than that of similar algorithms. It is fully proved that our algorithm can realize a more continuous and accurate position and attitude estimation and map reconstruction in urban environments. Full article

(This article belongs to the Special Issue Advanced Technologies for Position and Navigation under GNSS Signal Challenging or Denied Environments)

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21 pages, 4571 KiB

Open AccessArticle

A Shrink-Branch-Bound Algorithm for eLoran Pseudorange Positioning Initialization

by Kaiqi Liu, Jiangbin Yuan, Wenhe Yan, Chaozhong Yang, Wei Guo, Shifeng Li and Yu Hua

Remote Sens. 2022, 14(8), 1781; https://doi.org/10.3390/rs14081781 - 7 Apr 2022

Cited by 6 | Viewed by 2208

Abstract

Currently, eLoran is the ideal backup and supplement for global navigation satellite systems. The time synchronization accuracy between stations in the eLoran system has improved, providing conditions for eLoran pseudorange positioning. The pseudorange positioning of eLoran is a nonlinear least-squares problem and the [...] Read more.

Currently, eLoran is the ideal backup and supplement for global navigation satellite systems. The time synchronization accuracy between stations in the eLoran system has improved, providing conditions for eLoran pseudorange positioning. The pseudorange positioning of eLoran is a nonlinear least-squares problem and the location of the eLoran transmitting stations may cause the above problem to be non-convex. This makes the conventional pseudorange positioning al-gorithm strongly depend on the initial value when solving the eLoran pseudorange positioning. We propose a shrink-branch-bound (SBB) algorithm to solve the eLoran pseudorange positioning initialization problem. The algorithm first uses a shrink method to reduce the search space of the position estimator. Then, optimization is performed using a branch and bound algorithm within the shrunk region, where a trust region reflective algorithm is used for the lower bound process. The algorithm can help the receiver to complete the initial positioning without any initial value information. Simulation experiments verify that the algorithm has a success rate of more than 99.5% in solving the initialization problem of eLoran pseudorange positioning, and can be used as an initialization algorithm for pseudorange positioning problems for eLoran or other long-range terrestrial-based radio navigation system. Full article

(This article belongs to the Special Issue Advanced Technologies for Position and Navigation under GNSS Signal Challenging or Denied Environments)

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15 pages, 3190 KiB

Open AccessArticle

Intensity/Inertial Integration-Aided Feature Tracking on Event Cameras

by Zeyu Li, Yong Liu, Feng Zhou and Xiaowan Li

Remote Sens. 2022, 14(8), 1773; https://doi.org/10.3390/rs14081773 - 7 Apr 2022

Viewed by 2232

Abstract

Achieving efficient and accurate feature tracking on event cameras is a fundamental step for practical high-level applications, such as simultaneous localization and mapping (SLAM) and structure from motion (SfM) and visual odometry (VO) in GNSS (Global Navigation Satellite System)-denied environments. Although many asynchronous [...] Read more.

Achieving efficient and accurate feature tracking on event cameras is a fundamental step for practical high-level applications, such as simultaneous localization and mapping (SLAM) and structure from motion (SfM) and visual odometry (VO) in GNSS (Global Navigation Satellite System)-denied environments. Although many asynchronous tracking methods purely using event flow have been proposed, they suffer from high computation demand and drift problems. In this paper, event information is still processed in the form of synthetic event frames to better adapt to the practical demands. Weighted fusion of multiple hypothesis testing with batch processing (WF-MHT-BP) is proposed based on loose integration of event, intensity, and inertial information. More specifically, with inertial information acting as priors, multiple hypothesis testing with batch processing (MHT-BP) produces coarse feature-tracking solutions on event frames in a batch processing way. With a time-related stochastic model, a weighted fusion mechanism fuses feature-tracking solutions from event and intensity frames compared with other state-of-the-art feature-tracking methods on event cameras. Evaluation on public datasets shows significant improvements on accuracy and efficiency and comparable performances in terms of feature-tracking length. Full article

(This article belongs to the Special Issue Advanced Technologies for Position and Navigation under GNSS Signal Challenging or Denied Environments)

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18 pages, 4673 KiB

Open AccessArticle

A Comparison of the Performances of Unmanned-Aerial-Vehicle (UAV) and Terrestrial Laser Scanning for Forest Plot Canopy Cover Estimation in Pinus massoniana Forests

by Wenxia Dai, Qingfeng Guan, Shangshu Cai, Rundong Liu, Ruibo Chen, Qing Liu, Chao Chen and Zhen Dong

Remote Sens. 2022, 14(5), 1188; https://doi.org/10.3390/rs14051188 - 28 Feb 2022

Cited by 7 | Viewed by 2824

Abstract

Canopy cover is an important indicator and commonly used in forest management applications. Unmanned-Aerial-Vehicle (UAV)—Borne Laser Scanning (ULS) has drawn increasing attention as a new alternative source for forest field inventory due to its spatial resolution comparable to that of Terrestrial Laser Scanning [...] Read more.

Canopy cover is an important indicator and commonly used in forest management applications. Unmanned-Aerial-Vehicle (UAV)—Borne Laser Scanning (ULS) has drawn increasing attention as a new alternative source for forest field inventory due to its spatial resolution comparable to that of Terrestrial Laser Scanning (TLS). In this study, the performance of plot canopy cover estimations from ULS and TLS is investigated. The experiment was conducted in 16 plots from two Pinus massoniana forests with different stand conditions in Guangxi, China. Both the Canopy Height Model (CHM)-based and Individual Tree Delineation (ITD)-based methods were used to estimate the canopy cover. The influence of CHM pixel sizes on the estimations was also analyzed. Our results demonstrated that the accuracies of ULS (

R^{2}

: 0.992–0.996,

R M S E

: 0.591–0.820%) were better than those of TLS (

R^{2}

: 0.541–0.846,

R M S E

: 3.642–6.297%) when compared against the reference. The average difference between the ULS and TLS estimations was 6.91%, and the disagreement increased as the forest complexity increased. The reasonable CHM pixel sizes for the canopy cover estimations were 0.07–1.2 m for ULS and 0.07–1.5 m for TLS. This study can provide useful information for the selection of data sources and estimation methods in plot canopy cover mapping. Full article

(This article belongs to the Special Issue Advanced Technologies for Position and Navigation under GNSS Signal Challenging or Denied Environments)

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23 pages, 98518 KiB

Open AccessArticle

LiDAR-Visual-Inertial Odometry Based on Optimized Visual Point-Line Features

by Xuan He, Wang Gao, Chuanzhen Sheng, Ziteng Zhang, Shuguo Pan, Lijin Duan, Hui Zhang and Xinyu Lu

Remote Sens. 2022, 14(3), 622; https://doi.org/10.3390/rs14030622 - 27 Jan 2022

Cited by 17 | Viewed by 5840

Abstract

This study presents a LiDAR-Visual-Inertial Odometry (LVIO) based on optimized visual point-line features, which can effectively compensate for the limitations of a single sensor in real-time localization and mapping. Firstly, an improved line feature extraction in scale space and constraint matching strategy, using [...] Read more.

This study presents a LiDAR-Visual-Inertial Odometry (LVIO) based on optimized visual point-line features, which can effectively compensate for the limitations of a single sensor in real-time localization and mapping. Firstly, an improved line feature extraction in scale space and constraint matching strategy, using the least square method, is proposed to provide a richer visual feature for the front-end of LVIO. Secondly, multi-frame LiDAR point clouds were projected into the visual frame for feature depth correlation. Thirdly, the initial estimation results of Visual-Inertial Odometry (VIO) were carried out to optimize the scanning matching accuracy of LiDAR. Finally, a factor graph based on Bayesian network is proposed to build the LVIO fusion system, in which GNSS factor and loop factor are introduced to constrain LVIO globally. The evaluations on indoor and outdoor datasets show that the proposed algorithm is superior to other state-of-the-art algorithms in real-time efficiency, positioning accuracy, and mapping effect. Specifically, the average RMSE of absolute trajectory in the indoor environment is 0.075 m and that in the outdoor environment is 3.77 m. These experimental results can prove that the proposed algorithm can effectively solve the problem of line feature mismatching and the accumulated error of local sensors in mobile carrier positioning. Full article

(This article belongs to the Special Issue Advanced Technologies for Position and Navigation under GNSS Signal Challenging or Denied Environments)

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22 pages, 9908 KiB

Open AccessArticle

Low-Cost Single-Frequency DGNSS/DBA Combined Positioning Research and Performance Evaluation

by Shengliang Wang, Xianshu Dong, Genyou Liu, Ming Gao, Wenhao Zhao, Dong Lv and Shilong Cao

Remote Sens. 2022, 14(3), 586; https://doi.org/10.3390/rs14030586 - 26 Jan 2022

Cited by 6 | Viewed by 2823

Abstract

In recent years, low-cost single-frequency GNSS receivers have been widely used in many fields such as mass navigation and deformation monitoring; however, due to the poor signal quality of low-cost patch antennae, it is difficult for carrier phase real-time kinematic (RTK) technology to [...] Read more.

In recent years, low-cost single-frequency GNSS receivers have been widely used in many fields such as mass navigation and deformation monitoring; however, due to the poor signal quality of low-cost patch antennae, it is difficult for carrier phase real-time kinematic (RTK) technology to fix the integer ambiguity. Differential GNSS (DGNSS) positioning with pseudorange can effectively meet the high robustness and reliability requirements for the submeter to the meter level positioning accuracy of UVA/vehicle/aerospace users. To improve the DGNSS positioning accuracy and reliability of low-cost single-frequency GNSS receivers in complex environments, we propose a differential barometric altimetry (DBA)-assisted DGNSS positioning algorithm, which solves the DGNSS observation equations jointly and rigorously with the Earth ellipsoidal constraint equations constructed by the DBA altitude. The DBA altitude accuracy at different baseline lengths was evaluated in detail, and the DGNSS positioning performance of the single-frequency low-cost u-blox receiver NEO-M8T with a patch antenna and DGNSS/DBA combined positioning performance with the BMP280 barometer was analyzed by several sets of static and dynamic experiments under different environments. The results show that the single-frequency NEO-M8T receiver with patch antenna DGNSS positioning accuracy is submeter level in the static environment and drops to meter level in the dynamic environment. GPS+BDS dual system has higher positioning accuracy than single GPS or single BDS. DGNSS/DBA combination has higher positioning accuracy than DGNSS, especially the root mean square error (RMSE) can be improved by 30% to 80% in the U direction and slightly improved in the N and E directions. This study can provide an effective solution reference for various applications of low-cost sensor fusion positioning in the mass consumer market. Full article

(This article belongs to the Special Issue Advanced Technologies for Position and Navigation under GNSS Signal Challenging or Denied Environments)

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41 pages, 7840 KiB

Open AccessReview

Simultaneous Localization and Mapping (SLAM) for Autonomous Driving: Concept and Analysis

by Shuran Zheng, Jinling Wang, Chris Rizos, Weidong Ding and Ahmed El-Mowafy

Remote Sens. 2023, 15(4), 1156; https://doi.org/10.3390/rs15041156 - 20 Feb 2023

Cited by 36 | Viewed by 19335

Abstract

The Simultaneous Localization and Mapping (SLAM) technique has achieved astonishing progress over the last few decades and has generated considerable interest in the autonomous driving community. With its conceptual roots in navigation and mapping, SLAM outperforms some traditional positioning and localization techniques since [...] Read more.

The Simultaneous Localization and Mapping (SLAM) technique has achieved astonishing progress over the last few decades and has generated considerable interest in the autonomous driving community. With its conceptual roots in navigation and mapping, SLAM outperforms some traditional positioning and localization techniques since it can support more reliable and robust localization, planning, and controlling to meet some key criteria for autonomous driving. In this study the authors first give an overview of the different SLAM implementation approaches and then discuss the applications of SLAM for autonomous driving with respect to different driving scenarios, vehicle system components and the characteristics of the SLAM approaches. The authors then discuss some challenging issues and current solutions when applying SLAM for autonomous driving. Some quantitative quality analysis means to evaluate the characteristics and performance of SLAM systems and to monitor the risk in SLAM estimation are reviewed. In addition, this study describes a real-world road test to demonstrate a multi-sensor-based modernized SLAM procedure for autonomous driving. The numerical results show that a high-precision 3D point cloud map can be generated by the SLAM procedure with the integration of Lidar and GNSS/INS. Online four–five cm accuracy localization solution can be achieved based on this pre-generated map and online Lidar scan matching with a tightly fused inertial system. Full article

(This article belongs to the Special Issue Advanced Technologies for Position and Navigation under GNSS Signal Challenging or Denied Environments)

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27 pages, 21283 KiB

Open AccessReview

A Review of Multi-Sensor Fusion SLAM Systems Based on 3D LIDAR

by Xiaobin Xu, Lei Zhang, Jian Yang, Chenfei Cao, Wen Wang, Yingying Ran, Zhiying Tan and Minzhou Luo

Remote Sens. 2022, 14(12), 2835; https://doi.org/10.3390/rs14122835 - 13 Jun 2022

Cited by 142 | Viewed by 27446

Abstract

The ability of intelligent unmanned platforms to achieve autonomous navigation and positioning in a large-scale environment has become increasingly demanding, in which LIDAR-based Simultaneous Localization and Mapping (SLAM) is the mainstream of research schemes. However, the LIDAR-based SLAM system will degenerate and affect [...] Read more.

The ability of intelligent unmanned platforms to achieve autonomous navigation and positioning in a large-scale environment has become increasingly demanding, in which LIDAR-based Simultaneous Localization and Mapping (SLAM) is the mainstream of research schemes. However, the LIDAR-based SLAM system will degenerate and affect the localization and mapping effects in extreme environments with high dynamics or sparse features. In recent years, a large number of LIDAR-based multi-sensor fusion SLAM works have emerged in order to obtain a more stable and robust system. In this work, the development process of LIDAR-based multi-sensor fusion SLAM and the latest research work are highlighted. After summarizing the basic idea of SLAM and the necessity of multi-sensor fusion, this paper introduces the basic principles and recent work of multi-sensor fusion in detail from four aspects based on the types of fused sensors and data coupling methods. Meanwhile, we review some SLAM datasets and compare the performance of five open-source algorithms using the UrbanNav dataset. Finally, the development trend and popular research directions of SLAM based on 3D LIDAR multi-sensor fusion are discussed and summarized. Full article

(This article belongs to the Special Issue Advanced Technologies for Position and Navigation under GNSS Signal Challenging or Denied Environments)

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