Sparse optimization for robust and efficient loop closing
Name
Leonard_Sparse optimization.pdf
Description
Accepted version
Size
5.74 MB
Format
Adobe PDF
Checksum (MD5)
289802241080c4273fff8f23df39bd50
Author(s)
Latif, Yasir
Huang, Guoquan
Leonard, John Joseph
Neira, José
Date Issued
April 2017
Journal
Robotics and Autonomous Systems
Publisher
Elsevier BV
Citation
Latif, Yasir et al. "Sparse optimization for robust and efficient loop closing." Robotics and Autonomous Systems 93 (July 2017): 13-26 © 2017 Elsevier B.V.
Version
Original manuscript
Abstract
It is essential for a robot to be able to detect revisits or loop closures for long-term visual navigation. A key insight explored in this work is that the loop-closing event inherently occurs sparsely, i.e., the image currently being taken matches with only a small subset (if any) of previous images. Based on this observation, we formulate the problem of loop-closure detection as a sparse, convexℓ1-minimization problem. By leveraging fast convex optimization techniques, we are able to efficiently find loop closures, thus enabling real-time robot navigation. This novel formulation requires no offline dictionary learning, as required by most existing approaches, and thus allows online incremental operation. Our approach ensures a unique hypothesis by choosing only a single globally optimal match when making a loop-closure decision. Furthermore, the proposed formulation enjoys a flexible representation with no restriction imposed on how images should be represented, while requiring only that the representations are “close” to each other when the corresponding images are visually similar. The proposed algorithm is validated extensively using real-world datasets. Keywords: SLAM; Place recognition; Relocalization;
Sparse optimization
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1016/j.robot.2017.03.016
