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

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2 Author(s)
Trung-Dung Vu ; INRIA Rhône Alpes, Grenoble, France ; Olivier Aycard

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.

Published in:

Robotics and Automation, 2009. ICRA '09. IEEE International Conference on

Date of Conference:

12-17 May 2009