2026-09-19T00:18:53Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1399352022-02-08T03:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Carlone, Luca
Herrera Arias, Luis Fernando
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2022-02-07T15:13:45Z
2022-02-07T15:13:45Z
2021-09
2021-11-03T19:25:42.083Z
https://hdl.handle.net/1721.1/139935
Simultaneous Localization and Mapping (SLAM) is the capability to estimate a robot’s trajectory in an initially unknown environment while reconstructing the geometry of the environment. In order to bound the accumulation of localization error in SLAM, it is crucial to recognize previously seen locations, a process called "loop closure." This allows the robot to make corrections to its localization and map estimates. This project evaluates ORB feature extraction and matching, a state-of-the-art technique to detect loop closures, against recently developed learning-based approaches. In particular, our first contribution is to benchmark established techniques based on hand-crafted descriptor matching against novel learning-based approaches based on neural networks (i.e., SuperPoint and SuperGlue). As a second contribution, we integrate a learning-based loop closure detection method as part of Kimera, a SLAM system, and demonstrate its performance in both simulated and real benchmarking datasets. Finally, we collect data on long trajectories using a Jackal robot to compare the different approaches on real-world situations beyond available datasets. Our evaluation shows that, while learning-based approaches detect many more loop closures across wider baselines, when integrated in a SLAM system, they do not lead to substantial performance improvements compared to standard ORB feature matching.
M.Eng.
Massachusetts Institute of Technology
In Copyright - Educational Use Permitted
Copyright MIT
http://rightsstatements.org/page/InC-EDU/1.0/
An Experimental Evaluation of Learning-Based Methods for Loop Closure Detection in Simultaneous Localization and Mapping
Thesis
application/pdf
Master
Master of Engineering in Electrical Engineering and Computer Science
Publication
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unknown
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<Abstract>Simultaneous Localization and Mapping (SLAM) is the capability to estimate a robot’s trajectory in an initially unknown environment while reconstructing the geometry of the environment. In order to bound the accumulation of localization error in SLAM, it is crucial to recognize previously seen locations, a process called "loop closure." This allows the robot to make corrections to its localization and map estimates. This project evaluates ORB feature extraction and matching, a state-of-the-art technique to detect loop closures, against recently developed learning-based approaches. In particular, our first contribution is to benchmark established techniques based on hand-crafted descriptor matching against novel learning-based approaches based on neural networks (i.e., SuperPoint and SuperGlue). As a second contribution, we integrate a learning-based loop closure detection method as part of Kimera, a SLAM system, and demonstrate its performance in both simulated and real benchmarking datasets. Finally, we collect data on long trajectories using a Jackal robot to compare the different approaches on real-world situations beyond available datasets. Our evaluation shows that, while learning-based approaches detect many more loop closures across wider baselines, when integrated in a SLAM system, they do not lead to substantial performance improvements compared to standard ORB feature matching.</Abstract>
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