Novel method for monocular vision based mobile robot localization

A robust environment map with 3D spatial natural landmarks that facilitates monocular vision based mobile robot for global localization is built. The highly distinctive multi-dimensional vector descriptors associated with the features extracted through Scale Invariant Feature Transform (SIFT) can be robustly matched despite changes in illumination, scale and viewpoint. The landmarks are 3D restructured with the matching image feature pairs obtained through the KD-Tree based nearest search approach. Novel RANSAC approach based on generic optimization for global localization is presented. Experiments on the robot Pioneer3 with monocular vision in our real indoor environment show that our method is of high precision.