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Conference papers

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

Trung-Dung Vu 1, * Olivier Aycard 1, 2, * 
* Corresponding author
1 E-MOTION - Geometry and Probability for Motion and Action
Inria Grenoble - Rhône-Alpes, LIG - Laboratoire d'Informatique de Grenoble
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.
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Submitted on : Tuesday, July 8, 2014 - 4:05:37 PM
Last modification on : Wednesday, July 6, 2022 - 4:22:38 AM
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Trung-Dung Vu, Olivier Aycard. Laser-based detection and tracking moving objects using data-driven Markov chain Monte Carlo. Robotics and Automation, 2009. ICRA '09. IEEE International Conference on, May 2009, Kobe, Japan. pp.3800 - 3806, ⟨10.1109/ROBOT.2009.5152805⟩. ⟨hal-01020868⟩



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