Sparse optimization for robust and efficient loop closing
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 image...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Elsevier BV
2020
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| Online Access: | https://hdl.handle.net/1721.1/124489 |
| _version_ | 1826212129857863680 |
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| author | Latif, Yasir Huang, Guoquan Leonard, John Joseph Neira, José |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Latif, Yasir Huang, Guoquan Leonard, John Joseph Neira, José |
| author_sort | Latif, Yasir |
| collection | MIT |
| description | 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 |
| first_indexed | 2024-09-23T15:16:50Z |
| format | Article |
| id | mit-1721.1/124489 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T15:16:50Z |
| publishDate | 2020 |
| publisher | Elsevier BV |
| record_format | dspace |
| spelling | mit-1721.1/1244892022-10-02T01:53:50Z Sparse optimization for robust and efficient loop closing Latif, Yasir Huang, Guoquan Leonard, John Joseph Neira, José Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory 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 MINECO-FEDER project (DPI2015-68905-P) NSF (IIS-1318392) NSF (IIS-15661293) DTRA award HDTRA (1-16-1-0039) 2020-04-06T13:43:54Z 2020-04-06T13:43:54Z 2017-04 2017-01 2019-09-23T11:22:44Z Article http://purl.org/eprint/type/JournalArticle 0921-8890 https://hdl.handle.net/1721.1/124489 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. en http://dx.doi.org/10.1016/j.robot.2017.03.016 Robotics and Autonomous Systems Creative Commons Attribution-NonCommercial-NoDerivs License http://creativecommons.org/licenses/by-nc-nd/4.0/ application/pdf Elsevier BV arXiv |
| spellingShingle | Latif, Yasir Huang, Guoquan Leonard, John Joseph Neira, José Sparse optimization for robust and efficient loop closing |
| title | Sparse optimization for robust and efficient loop closing |
| title_full | Sparse optimization for robust and efficient loop closing |
| title_fullStr | Sparse optimization for robust and efficient loop closing |
| title_full_unstemmed | Sparse optimization for robust and efficient loop closing |
| title_short | Sparse optimization for robust and efficient loop closing |
| title_sort | sparse optimization for robust and efficient loop closing |
| url | https://hdl.handle.net/1721.1/124489 |
| work_keys_str_mv | AT latifyasir sparseoptimizationforrobustandefficientloopclosing AT huangguoquan sparseoptimizationforrobustandefficientloopclosing AT leonardjohnjoseph sparseoptimizationforrobustandefficientloopclosing AT neirajose sparseoptimizationforrobustandefficientloopclosing |