Robot Navigation Map Building Using Stereo Vision Based 3D Occupancy Grid

In this paper environment is modeled using depth information provided by a stereo vision system. Workspace is decomposed into voxels which are the smallest volume of environment. A rst observation on the state of the voxels is calculated based on stereo system provided 3D points and triangulation error propagation. A new method for model update using prior and current observations on the voxel state is presented. The proposed update function uses a credibility value that denotes how strongly a new observation shall in uence the voxel state based on the age of the last observation and the homogeneity of the current observations. Finally, the 3D occupancy grid is scaled down to a 2D map to reduce computational costs. Experimental results using real environment and comparison based on a benchmarking method are presented to demonstrate the performance of our approach.