Stereo Depth Map Fusion for Robot Navigation

We present a method to reconstruct indoor environments from stereo image pairs, suitable for the navigation of robots. To enable a robot to navigate solely using visual cues it receives from a stereo camera, the depth information needs to be extracted from the image pairs and combined into a common representation. The initially determined raw depthmaps are fused into a two level heightmap representation which contains a floor and a ceiling height level. To reduce the noise in the height maps we employ a total variation regularized energy functional. With this 2.5D representation of the scene the computational complexity of the energy optimization is reduced by one dimension in contrast to other fusion techniques that work on the full 3D space such as volumetric fusion. While we show only results for indoor environments the approach can be extended to generate heightmaps for outdoor environments.