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https://hdl.handle.net/2440/133225
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Type: | Conference paper |
Title: | Visual SLAM: Why bundle adjust? |
Author: | Parra Bustos, A. Chin, T.J. Eriksson, A. Reid, I. |
Citation: | IEEE International Conference on Robotics and Automation, 2019 / Howard, A., Althoefer, K., Arai, F., Arrichiello, F., Caputo, B., Castellanos, J., Hauser, K., Isler, V., Kim, J., Liu, H., Oh, P., Santos, V., Scaramuzza, D., Ude, A., Voyles, R., Yamane, K., Okamura, A. (ed./s), vol.2019-May, pp.2385-2391 |
Publisher: | IEEE |
Publisher Place: | online |
Issue Date: | 2019 |
Series/Report no.: | IEEE International Conference on Robotics and Automation ICRA |
ISBN: | 9781538660263 |
ISSN: | 1050-4729 2577-087X |
Conference Name: | International Conference on Robotics and Automation (ICRA) (20 May 2019 - 24 May 2019 : Montreal, Canada) |
Editor: | Howard, A. Althoefer, K. Arai, F. Arrichiello, F. Caputo, B. Castellanos, J. Hauser, K. Isler, V. Kim, J. Liu, H. Oh, P. Santos, V. Scaramuzza, D. Ude, A. Voyles, R. Yamane, K. Okamura, A. |
Statement of Responsibility: | Álvaro Parra Bustos, Tat-Jun Chin, Anders Eriksson, and Ian Reid |
Abstract: | Bundle adjustment plays a vital role in featurebased monocular SLAM. In many modern SLAM pipelines, bundle adjustment is performed to estimate the 6DOF camera trajectory and 3D map (3D point cloud) from the input feature tracks. However, two fundamental weaknesses plague SLAM systems based on bundle adjustment. First, the need to carefully initialise bundle adjustment means that all variables, in particular the map, must be estimated as accurately as possible and maintained over time, which makes the overall algorithm cumbersome. Second, since estimating the 3D structure (which requires sufficient baseline) is inherent in bundle adjustment, the SLAM algorithm will encounter difficulties during periods of slow motion or pure rotational motion. We propose a different SLAM optimisation core: instead of bundle adjustment, we conduct rotation averaging to incrementally optimise only camera orientations. Given the orientations, we estimate the camera positions and 3D points via a quasiconvex formulation that can be solved efficiently and globally optimally. Our approach not only obviates the need to estimate and maintain the positions and 3D map at keyframe rate (which enables simpler SLAM systems), it is also more capable of handling slow motions or pure rotational motions. |
Keywords: | Simultaneous localization and mapping; Cameras; Estimation |
Rights: | © 2019 IEEE |
DOI: | 10.1109/ICRA.2019.8793749 |
Grant ID: | http://purl.org/au-research/grants/arc/DP160103490 http://purl.org/au-research/grants/arc/CE140100016 |
Published version: | http://dx.doi.org/10.1109/icra.2019.8793749 |
Appears in Collections: | Computer Science publications |
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