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Fast Object Extraction from Bayesian Occupancy Grids Using Self Organizing Networks

Dizan Alejandro Vasquez Govea 1 Fabrizio Romanelli 1 Thierry Fraichard 1 Christian Laugier 1
1 E-MOTION - Geometry and Probability for Motion and Action
GRAVIR - IMAG - Graphisme, Vision et Robotique, Inria Grenoble - Rhône-Alpes
Abstract : Despite their popularity, occupancy grids cannot be directly applied to problems where the identity of the objects populating an environment needs to be taken into account (eg object tracking, scene interpretation, etc), in this cases it is necessary to postprocess the grid in order to extract object information. This paper approaches the problem by proposing a novel algorithm inspired on image segmentation techniques. The proposed approach works without prior knowledge about the number of objects to be detected and, at the same time, is very fast. This is possible thanks to the use of a novel Self Organizing Network (SON) coupled with a dynamic threshold. Our experimental results on both real and simulated data show that our approach is robust and able to operate at normal camera framerate.
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Contributor : Christian Laugier <>
Submitted on : Wednesday, October 24, 2007 - 6:06:38 PM
Last modification on : Friday, June 26, 2020 - 4:04:02 PM
Long-term archiving on: : Monday, April 12, 2010 - 12:30:14 AM


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  • HAL Id : inria-00182011, version 1




Dizan Alejandro Vasquez Govea, Fabrizio Romanelli, Thierry Fraichard, Christian Laugier. Fast Object Extraction from Bayesian Occupancy Grids Using Self Organizing Networks. Proc. of the Int. Conf. on Control, Automation, Robotics and Vision (ICARCV), Dec 2006, Singapore (SG), France. ⟨inria-00182011⟩



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