Real-Time Stereo Visual SLAM in Large-Scale Environments based on SIFT Fingerprints
- Schleicher, David; Bergasa, Luis M.; Barea, Rafael; López, Elena; Ocaña, Manuel; Nuevo, Jesús; Alcantarilla, Pablo F.
- Year: 2007
- 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
- Pages: 1 -6
- Month: oct.
- ISBN: 978-1-4244-0830-6
- DOI: 10.1109/WISP.2007.4447566
- 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.