TY - GEN
T1 - Robust selective stereo SLAM without loop closure and bundle adjustment
AU - Bellavia, Fabio
AU - Bellavia, Fabio
AU - Fanfani, Marco
AU - Pazzaglia, Fabio
AU - Colombo, Carlo
PY - 2013
Y1 - 2013
N2 - This paper presents a novel stereo SLAM framework, where a robust loop chain matching scheme for tracking keypoints is combined with an effective frame selection strategy. The proposed approach, referred to as selective SLAM (SSLAM), relies on the observation that the error in the pose estimation propagates from the uncertainty of the three-dimensional points. This is higher for distant points, corresponding to matches with low temporal flow disparity in the images. Comparative results based on the reference KITTI evaluation framework show that SSLAM is effective and can be implemented efficiently, as it does not require any loop closure or bundle adjustment.
AB - This paper presents a novel stereo SLAM framework, where a robust loop chain matching scheme for tracking keypoints is combined with an effective frame selection strategy. The proposed approach, referred to as selective SLAM (SSLAM), relies on the observation that the error in the pose estimation propagates from the uncertainty of the three-dimensional points. This is higher for distant points, corresponding to matches with low temporal flow disparity in the images. Comparative results based on the reference KITTI evaluation framework show that SSLAM is effective and can be implemented efficiently, as it does not require any loop closure or bundle adjustment.
UR - http://hdl.handle.net/10447/385529
UR - https://link.springer.com/content/pdf/10.1007/978-3-642-41181-6_47.pdf
M3 - Conference contribution
SN - 978-3-642-41180-9; 978-3-642-41181-6
T3 - LECTURE NOTES IN COMPUTER SCIENCE
SP - 462
EP - 471
BT - Image Analysis and Processing – ICIAP 2013
ER -