The “Fast Clustering-Tracking” algorithm in the Bayesian occupancy Filter framework

Kamel Mekhnacha 1 Yong Mao 2 David Raulo 1 Christian Laugier 2
2 E-MOTION - Geometry and Probability for Motion and Action
Inria Grenoble - Rhône-Alpes, LIG - Laboratoire d'Informatique de Grenoble
Abstract : It has been shown that the dynamic environment around the mobile robot can be efficiently and robustly represented by the Bayesian occupancy filter(BOF). In the BOF framework, the physical world is decomposed into a grid based representation, concepts such as objects or tracks do not exist. However, the object level representation may be demanded by the applications. To achieve this, a novel object detecting and tracking algorithm is presented. The prediction result of the tracking module is used as a form of feedback to the clustering module. The data association problem is taking into account by dealing with the ambiguous associations. Compared with the traditional joint probabilistic data association filter(JPDAF) method, the proposed algorithm demands less computational costs, so as to be suitable for environments with large amount of dynamic objects. The experiment result on the real data shows the effectiveness of the algorithm.
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Communication dans un congrès
MFI2008, Aug 2008, Seoul, North Korea. 2008
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Kamel Mekhnacha, Yong Mao, David Raulo, Christian Laugier. The “Fast Clustering-Tracking” algorithm in the Bayesian occupancy Filter framework. MFI2008, Aug 2008, Seoul, North Korea. 2008. 〈inria-00336359〉

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