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Vehicle Localization Based on GNSS and In-Vehicle Sensors
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Vehicle Localization Based on GNSS and In-Vehicle Sensors

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Vehicular Sensing".

Deadline for manuscript submissions: closed (20 February 2023) | Viewed by 12071

Special Issue Editor

Dr. Kichun Jo

E-Mail Website
Guest Editor
Department of Smart Vehicle Engineering, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul 05029, Korea
Interests: localization; mapping; SLAM; dynamic HD map; sensor fusion; behavior and trajectory planning for autonomous car
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Vehicle localization, which accurately and precisely estimates the position of a vehicle, is essential for intelligent vehicles, including autonomous cars, mobile robots, and unmanned aerial vehicles (UAV). GNSS and in-vehicle sensors, such as inertial measurement unit, camera, LiDAR, and radar, are widely used, with various types of localization algorithms to obtain accurate and precise vehicle position. This Special Issue focuses on vehicle localization based on GNSS and in-vehicle sensors. We welcome original research contributions and state-of-the-art reviews, from academia and the industry. Potential topics include, but are not limited to:

  • GNSS signal processing and positioning;
  • in-vehicle sensors for vehicle localization;
  • algorithms for localization and mapping;
  • applications based on the vehicle localization.

Dr. Kichun Jo
Guest Editor

Manuscript Submission Information

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Keywords

  • GNSS positioning
  • inertial measurement unit
  • camera
  • LiDAR
  • radar
  • inertial navigation system
  • dead reckoning
  • visual odometry
  • LiDAR odometry
  • sensor fusion
  • dynamic filtering (Kalman filter, particle filter, etc.)
  • machine learning
  • simultaneously localization and mapping (SLAM)
  • autonomous car
  • mobile robot
  • unmanned aerial vehicle (UAV)

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Published Papers (3 papers)

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Research

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21 pages, 5345 KiB  
Article
Feature-Based Occupancy Map-Merging for Collaborative SLAM
by Sooraj Sunil, Saeed Mozaffari, Rajmeet Singh, Behnam Shahrrava and Shahpour Alirezaee
Sensors 2023, 23(6), 3114; https://doi.org/10.3390/s23063114 - 14 Mar 2023
Cited by 7 | Viewed by 3629
Abstract
One of the most frequently used approaches to represent collaborative mapping are probabilistic occupancy grid maps. These maps can be exchanged and integrated among robots to reduce the overall exploration time, which is the main advantage of the collaborative systems. Such map fusion [...] Read more.
One of the most frequently used approaches to represent collaborative mapping are probabilistic occupancy grid maps. These maps can be exchanged and integrated among robots to reduce the overall exploration time, which is the main advantage of the collaborative systems. Such map fusion requires solving the unknown initial correspondence problem. This article presents an effective feature-based map fusion approach that includes processing the spatial occupancy probabilities and detecting features based on locally adaptive nonlinear diffusion filtering. We also present a procedure to verify and accept the correct transformation to avoid ambiguous map merging. Further, a global grid fusion strategy based on the Bayesian inference, which is independent of the order of merging, is also provided. It is shown that the presented method is suitable for identifying geometrically consistent features across various mapping conditions, such as low overlapping and different grid resolutions. We also present the results based on hierarchical map fusion to merge six individual maps at once in order to constrict a consistent global map for SLAM. Full article
(This article belongs to the Special Issue Vehicle Localization Based on GNSS and In-Vehicle Sensors)
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18 pages, 420 KiB  
Article
Student’s t-Kernel-Based Maximum Correntropy Kalman Filter
by Hongliang Huang and Hai Zhang
Sensors 2022, 22(4), 1683; https://doi.org/10.3390/s22041683 - 21 Feb 2022
Cited by 10 | Viewed by 2695
Abstract
The state estimation problem is ubiquitous in many fields, and the common state estimation method is the Kalman filter. However, the Kalman filter is based on the mean square error criterion, which can only capture the second-order statistics of the noise and is [...] Read more.
The state estimation problem is ubiquitous in many fields, and the common state estimation method is the Kalman filter. However, the Kalman filter is based on the mean square error criterion, which can only capture the second-order statistics of the noise and is sensitive to large outliers. In many areas of engineering, the noise may be non-Gaussian and outliers may arise naturally. Therefore, the performance of the Kalman filter may deteriorate significantly in non-Gaussian noise environments. To improve the accuracy of the state estimation in this case, a novel filter named Student’s t kernel-based maximum correntropy Kalman filter is proposed in this paper. In addition, considering that the fixed-point iteration method is used to solve the optimal estimated state in the filtering algorithm, the convergence of the algorithm is also analyzed. Finally, comparative simulations are conducted and the results demonstrate that with the proper parameters of the kernel function, the proposed filter outperforms the other conventional filters, such as the Kalman filter, Huber-based filter, and maximum correntropy Kalman filter. Full article
(This article belongs to the Special Issue Vehicle Localization Based on GNSS and In-Vehicle Sensors)
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Review

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23 pages, 1290 KiB  
Review
Bio-Inspired Optimization-Based Path Planning Algorithms in Unmanned Aerial Vehicles: A Survey
by Sabitri Poudel, Muhammad Yeasir Arafat and Sangman Moh
Sensors 2023, 23(6), 3051; https://doi.org/10.3390/s23063051 - 12 Mar 2023
Cited by 36 | Viewed by 4683
Abstract
Advancements in electronics and software have enabled the rapid development of unmanned aerial vehicles (UAVs) and UAV-assisted applications. Although the mobility of UAVs allows for flexible deployment of networks, it introduces challenges regarding throughput, delay, cost, and energy. Therefore, path planning is an [...] Read more.
Advancements in electronics and software have enabled the rapid development of unmanned aerial vehicles (UAVs) and UAV-assisted applications. Although the mobility of UAVs allows for flexible deployment of networks, it introduces challenges regarding throughput, delay, cost, and energy. Therefore, path planning is an important aspect of UAV communications. Bio-inspired algorithms rely on the inspiration and principles of the biological evolution of nature to achieve robust survival techniques. However, the issues have many nonlinear constraints, which pose a number of problems such as time restrictions and high dimensionality. Recent trends tend to employ bio-inspired optimization algorithms, which are a potential method for handling difficult optimization problems, to address the issues associated with standard optimization algorithms. Focusing on these points, we investigate various bio-inspired algorithms for UAV path planning over the past decade. To the best of our knowledge, no survey on existing bio-inspired algorithms for UAV path planning has been reported in the literature. In this study, we investigate the prevailing bio-inspired algorithms extensively from the perspective of key features, working principles, advantages, and limitations. Subsequently, path planning algorithms are compared with each other in terms of their major features, characteristics, and performance factors. Furthermore, the challenges and future research trends in UAV path planning are summarized and discussed. Full article
(This article belongs to the Special Issue Vehicle Localization Based on GNSS and In-Vehicle Sensors)
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