Three-dimensional mapping utilizing stereo vision and Bayesian inference

In this study we propose a method for creating 3D map of real world environment by using 3D occupancy grids. The map is created by characterizing each grid associated with a certain area in the real world environment by utilizing multiple measurements using stereo vision and Bayesian inference. The proposed method can absorb the measurement uncertainties caused in the stereo matching process and in the system's calibrations. The preliminary experiments show that the proposed algorithm is able to robustly generate environment maps. The algorithm is also suitable to be implemented as a vision system for autonomous mobile robots.