GPS-denied Vehicle Localization for Augmented Reality Using a Road-Aided Particle Filter and RGB Camera

Welsh, Tomihisa F.; Marks, Sean M.; Pronschinske, Alex

doi:10.1109/plans53410.2023.10140123

2023 IEEE/ION Position, Location and Navigation Symposium (PLANS) 2023

DOI: 10.1109/plans53410.2023.10140123

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GPS-denied Vehicle Localization for Augmented Reality Using a Road-Aided Particle Filter and RGB Camera

Tomihisa F. Welsh

Sean M. Marks

Alex Pronschinske

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2023

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A Survey of Vehicle Localization: Performance Analysis and Challenges

Shan,

Cabani,

Chafouk

2023

IEEE Access

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Vehicle localization plays a crucial role in ensuring the safe operation of autonomous vehicles and the development of intelligent transportation systems (ITS). However, there is insufficient effort to compare the performance and challenges of different vehicle localization algorithms. This paper aims to address this gap by analyzing the comprehensive performance of existing advanced vehicle localization techniques and discussing their challenges. Firstly, we analyze the self-localization methods based on active and passive sensors. The results show that, the light detection and ranging (LiDAR) and vision-based techniques can reach high accuracy. However, they have high computational complexity. And only using the inertial measurement unit (IMU), global positioning system (GPS), radar, and ultrasonic sensors may not realize localization result with high accuracy. Then, we discuss V2X-based cooperative methods and analyze the multi-sensor based localization techniques and compare the comprehensive performance among all methods. Although the artificial intelligence (AI) techniques can effectively enhance the efficiency of vision-based localization algorithms, the high computational complexity still should be considered. In addition, since the IMU, GPS, radar, and ultrasonic sensors have good performance in terms of the availability, scalability, computational complexity, and cost-effectiveness, they can be used as auxiliary sensors to achieve good comprehensive performance through data fusion techniques. Finally, we propose the challenges of different techniques and look forward to future work.

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A Survey of Vehicle Localization: Performance Analysis and Challenges

Shan,

Cabani,

Chafouk

2023

IEEE Access

View full text Add to dashboard Cite

show abstract

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GPS-denied Vehicle Localization for Augmented Reality Using a Road-Aided Particle Filter and RGB Camera

Cited by 1 publication

References 14 publications

A Survey of Vehicle Localization: Performance Analysis and Challenges

A Survey of Vehicle Localization: Performance Analysis and Challenges

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