Autonomous Vehicle Localization via LiDAR-Based Classification of Dynamic and Static Tracks in Dynamic Environments

Autonomous vehicles (AVs) must be able to locate themselves accurately in dynamic environments in order to be able to navigate safely. In this paper, we present a robust LiDAR-based framework to improve the localization of AVs based on the classification of static and dynamic tracks. The approach leverages static tracks previously classified as reliable landmarks. Using Growing Neural Gas, Joint Probabilistic Data Association, and Unmotivated Kalman Filter algorithms, interaction dictionaries and vocabularies are generated. These serve as inputs for a Markov Jump Particle Filter (MJPF), which enables the accurate estimation of the ego-vehicle trajectory. Testing of the proposed framework with real-world LiDAR data demonstrates that it is capable of providing highly accurate localization results without the use of odometry, making it adaptable for use in environments without GPS. AV situational awareness and navigation performance are enhanced by using static tracks as robust references.