Abstract
This paper first demonstrates an interesting property of bundle adjustment (BA), "scale drift correction". Here "scale drift correction" means that BA can converge to the correct solution (up to a scale) even if the initial values of the camera pose translations and point feature positions are calculated using very different scale factors. This property together with other properties of BA makes it the best approach for monocular Simultaneous Localization and Mapping (SLAM), without considering the computational complexity. This naturally leads to the idea of using local BA and map joining to solve large-scale monocular SLAM problem, which is proposed in this paper. The local maps are built through Scale-Invariant Feature Transform (SIFT) for feature detection and matching, random sample consensus (RANSAC) paradigm at different levels for robust outlier removal, and BA for optimization. To reduce the computational cost of the large-scale map building, the features in each local map are judiciously selected and then the local maps are combined using a recently developed 3D map joining algorithm. The proposed large-scale monocular SLAM algorithm is evaluated using a publicly available dataset with centimeter-level ground truth.
Original language | English |
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Title of host publication | 2010 11th International Conference on Control Automation Robotics & Vision |
Publisher | Institute of Electrical and Electronics Engineers |
Pages | 431-436 |
Number of pages | 6 |
ISBN (Electronic) | 9781424478156 |
ISBN (Print) | 9781424478149 |
DOIs | |
Publication status | Published - 4 Feb 2011 |
Event | 11th International Conference on Control, Automation, Robotics and Vision - Singapore, Singapore Duration: 7 Dec 2010 → 10 Dec 2010 |
Conference
Conference | 11th International Conference on Control, Automation, Robotics and Vision |
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Abbreviated title | ICARCV 2010 |
Country/Territory | Singapore |
City | Singapore |
Period | 7/12/10 → 10/12/10 |
Keywords / Materials (for Non-textual outputs)
- bundle adjustment
- map joining
- visual SLAM