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

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 Filt...

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Bibliographic Details
Main Authors: Muhammad Adnan, Pamela Zontone, David Martín Gómez, Lucio Marcenaro, Carlo Regazzoni
Format: Article
Language:English
Published: MDPI AG 2025-05-01
Series:Applied Sciences
Subjects:
LiDAR-based localization
autonomous vehicle navigation
track classification
Dynamic Bayesian Networks
probabilistic modeling
anomaly detection
Online Access:https://www.mdpi.com/2076-3417/15/9/5181
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