University of Alcalá
|Type of Publication:||In Proceedings||Keywords:||3D sequential mapping;SIFT fingerprint;autonomous robot navigation;computer vision;large-scale environment;real-time stereo visual SLAM;scale invariant feature transform;top-down Bayesian method;wide-angle stereo camera;Bayes methods;SLAM;mobile|
|Book title:||Intelligent Signal Processing, 2007. WISP 2007. IEEE International Symposium on|
This paper presents a new method for real-time SLAM calculation applied to autonomous robot navigation in large-scale environments without restrictions. It is exclusively based on the visual 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 correction method based on the SIFT fingerprints taking for each sub-map. 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.
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