Large-Scale Monocular SLAM by Local Bundle Adjustment and Map Joining

Publication Type:
Conference Proceeding
Proc. of the 11th. Int. Conf. Control, Automation, Robotics and Vision (ICARCV 2010), 2010, pp. 431 - 436
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This paper first demonstrates an interesting property of bundle adjustment (BA), âscale drift correctionâ . Here âscale drift correctionâ 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.
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