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Conference Papers Year : 2015

Evidential occupancy grid mapping with stereo-vision


Occupancy grids have shown interesting properties to model the environment for intelligent vehicles perception. In this paper, we present a novel approach to build 2D occupancy grid maps with stereo-vision. Our approach proposes a fitted sensor model based on the disparity space to interpret the stereo-vision information onto an occupancy grid map. The evidential model deals with sensor uncertainties by using Dempster-Shafer theory. Our approach exploits the U-disparity space to model the obstacle information and the V-disparity space to model the road space information. The fusion of these two sources of complementary information results to an enhanced environmental model. In a first data set, experimental results based on real road data and comparisons with Lidar grids show that the proposed evidential sensor model can model efficiently the environment. In a second one, the mapping of a road environment is reported to show the performance of the proposed model with another stereo-vision system.
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hal-01261610 , version 1 (25-01-2016)



Chunlei Yu, Véronique Cherfaoui, Philippe Bonnifait. Evidential occupancy grid mapping with stereo-vision. IEEE Intelligent Vehicles Symposium (IV 2015), Jun 2015, Seoul, South Korea. pp.712-717, ⟨10.1109/IVS.2015.7225768⟩. ⟨hal-01261610⟩
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