A Generative Model Approach for LiDAR-Based Classification and Ego Vehicle Localization Using Dynamic Bayesian Networks
Source abstract
Abstract
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
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Autonomous Vehicle Technology and Safety · Robotics and Sensor-Based Localization · Remote Sensing and LiDAR Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Electrical, Electronic, Telecommunications Engineering and Naval Architecture (DITEN), University of Genova, Via Opera Pia 11a, I-16145 Genoa, Italy
Departamento de Ingeniería de Sistemas y Automática, Universidad Carlos III de Madrid, Butarque 15, 28911 Leganés, Madrid, Spain
Funding
European Union’s Horizon Europe research and innovation programme · European Union—NextGenerationEU · Spanish Government
References (54)
Show source-provided references
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