Real-time wide-angle stereo visual SLAM on large environments using SIFT features correction

This paper presents a new method for real-time SLAM calculation applied to autonomous robot navigation in large environments without restrictions. It is exclusively based on the information provided by a cheap wide-angle stereo camera. Our approach divide the global map into local sub- maps identified by the so-called SIFT fingerprint. At the sub- map level (low level SLAM), 3D sequential mapping of natural land-marks and the robot location/orientation are obtained using a top-down Bayesian method to model the dynamic behavior. A high abstraction level to reduce the global accumulated drift, keeping real-time constraints, has been added (high level SLAM). This uses a SIFT correction method based on the sub-maps' fingerprints. A comparison of the low SLAM level using our method and SIFT features has been carried out. Some experimental results using a real large environment are presented.