Laser-based detection and tracking moving objects using data-driven Markov chain Monte Carlo

Authors:
Trung-Dung Vu;Olivier Aycard
Affiliations:
INRIA Rhône Alpes, Grenoble, France;INRIA Rhône Alpes, Grenoble, France
Venue:
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Year:
2009

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Abstract

We present a method of simultaneous detection and tracking moving objects from a moving vehicle equipped with a single layer laser scanner. A model-based approach is introduced to interpret the laser measurement sequence by hypotheses of moving object trajectories over a sliding window of time. Knowledge of various aspects including object model, measurement model, motion model are integrated in one theoretically sound Bayesian framework. The data-driven Markov chain Monte Carlo (DDMCMC) technique is used to sample the solution space effectively to find the optimal solution. Experiments and results on real-life data of urban traffic show promising results.