A global 3D map-building approach using stereo vision

We present a stereo-based approach for building 3D maps. First, the best local alignment between successive point clouds is computed by a fast ego-motion/action-estimation algorithm which relies on an incremental matches filtering process followed by energy minimization. Then, a quasi-random updating algorithm, a kind of multi-view ICP, minimizes the global inconsistency of the map. Such an inconsistency is defined in terms of the sum of local inconsistencies and an additional entropy-based regularization term which is effective in plane-parallel environments. For the sake of efficiency, we assume a flat floor and a fixed stereo camera mounted on the robot. We have successfully tested the approach by performing several indoor mapping experiments.