Large-scale monocular SLAM by local bundle adjustment and map joining

Liang Zhao*, Shoudong Huang, Lei Yan, Jack Jianguo Wang, Gibson Hu, Gamini Dissanayake

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 languageEnglish
Title of host publication2010 11th International Conference on Control Automation Robotics & Vision
PublisherInstitute of Electrical and Electronics Engineers
Pages431-436
Number of pages6
ISBN (Electronic)9781424478156
ISBN (Print)9781424478149
DOIs
Publication statusPublished - 4 Feb 2011
Event11th International Conference on Control, Automation, Robotics and Vision - Singapore, Singapore
Duration: 7 Dec 201010 Dec 2010

Conference

Conference11th International Conference on Control, Automation, Robotics and Vision
Abbreviated titleICARCV 2010
Country/TerritorySingapore
CitySingapore
Period7/12/1010/12/10

Keywords / Materials (for Non-textual outputs)

  • bundle adjustment
  • map joining
  • visual SLAM

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