A 3D Omnidirectional Sensor For Mobile Robot Applications

Abstract

This paper has been devoted to the design of an innovative vision sensor dedicated to mobile robot application. Combining omnidirectional and stereoscopic vision offers many advantages for 3D reconstruction and navigation that are the two main tasks a robot has to achieve. In this article we have highlighted that the best stereoscopic configuration is the vertical one as it simplifies the pixel matching between images. A special care has been put on the sensor calibration to make it flexible since it only requires the use of a planar calibration pattern. Experimental results highlight a high accuracy, which foreshadows good results for the following algorithms. The fundamental issue of Simultaneous Localization And Mapping (SLAM) was then addressed. Our solution to this problem relies on a bundle adjustment between two displacements of the robot. The initialization of the motion and the coordinates of 3D points is a prerequisite since bundle adjustment is based on a non-linear minimization. This step is a tricky problem to which we answered by the generalization of the epipolar geometry for central sensors using the unified model. Our experimental results on SLAM are promising but the error is higher than the one expected further to the calibration results. Our future work will focus on the improvement of our SLAM method by adding a global bundle adjustment to avoid error accumulation. A lot of challenges in SLAM are moreover always open, for instance SLAM based only on vision systems, SLAM taking into account six degrees of freedom, or SLAM for large-scale mapping.