A Generative Model Approach for LiDAR-Based Classification and Ego Vehicle Localization Using Dynamic Bayesian Networks

Our work presents a robust framework for classifying static and dynamic tracks and localizing an ego vehicle in dynamic environments using LiDAR data. Our methodology leverages generative models, specifically Dynamic Bayesian Networks (DBNs), interaction dictionaries, and a Markov Jump Particle Filter (MJPF), to accurately classify objects within LiDAR point clouds and localize the ego vehicle without relying on external odometry data during testing. The classification phase effectively distinguishes between static and dynamic objects with high accuracy, achieving an F1 score of 91%. The localization phase utilizes a combined dictionary approach, integrating multiple static landmarks to improve robustness, particularly during simultaneous multi-track observations and no-observation intervals. Experimental results validate the efficacy of our proposed approach in enhancing localization accuracy and maintaining consistency in diverse scenarios

lidar-based localization; autonomous vehicle navigation; track classification; dynamic bayesian networks; probabilistic modeling; anomaly detection; interaction dictionaries; markov jump particle filter

A Generative Model Approach for LiDAR-Based Classification and Ego Vehicle Localization Using Dynamic Bayesian Networks Articles