The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Improved appearance-based matching in similar and dynamic environments using a Vocabulary tree
Abstract
In this paper we present a topological map building algorithm based on a Vocabulary Tree that is robust to features present in dynamic or similar environments. The algorithm recognises incorrect loop closures, not supported by the odometry, and uses this information to update the feature weights in the tree to suppress further associations from these features. Two methods of adjusting these feature entropies are proposed, one decreasing entropy related to incorrect features in a uniform manner and the other proportional to the contribution of the said feature. Preliminary results showing the performance of the proposed method are presented where it is found that by adjusting the feature entropies, the number of incorrect associations can be reduced while improving the quality of the correct matches.
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Other methods attempt to detect outliers a priori before optimization by identifying incorrect loop closures not supported by odometry.
“Other methods, instead, attempt to detect outliers a priori , that is, before any optimization takes place, by identifying incorrect loop closures that are not supported by the odometry [215].”
From Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age (2016) · cites this paper as [215]
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Cites
FAB-MAP: Probabilistic Localization and Mapping in the Space of Appearance
Cummins, M., & Newman, P. (2008) · The International Journal of Robotics Research · doi:10.1177/0278364908090961 · Cited by 1,472
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Research works printed but not linked: 19
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Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age
Cadena, C., Carlone, L., Carrillo, H., Latif, Y., Scaramuzza, D., Neira, J., Reid, I., & Leonard, J. J. (2016) · IEEE Transactions on Robotics · doi:10.1109/tro.2016.2624754 · Cited by 3,596
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Metadata
- Authors
- Sabatta, D., Scaramuzza, D., & Siegwart, R.
- Affiliations
- Council for Scientific and Industrial Research (ZA)
- ETH Zurich (CH)
- Venue
- 2010 IEEE International Conference on Robotics and Automation
- Publisher
- IEEE
- Published
- 2010-05
- Volume
- 1
- Pages
- 1008-1013
- ISSN
- 1050-4729 (ISSN-L)
- OpenAlex
- W2116134448
- Cited by
- 18
- References
- 1
- Citations by year
- 2018: 2 · 2016: 4 · 2014: 3 · 2013: 4 · 2012: 2
- FWCI
- 5.73
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Proceedings - IEEE International Conference on Robotics and Automation/Proceedings (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Robotics and Sensor-Based Localization
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Earth and Planetary Sciences › Geology › 3D Surveying and Cultural Heritage
- Keywords
- vocabulary tree, topological map building, loop closure detection, feature matching, dynamic environments, feature entropy, feature weighting, odometry, SLAM
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