Abstract
In this paper we present for the first time a relocalisation method for keyframe-based SLAM that can deal with severe viewpoint change, at frame-rate, in maps containing thousands of keyframes. As this method relies on local features, it permits the interoperability between cameras, allowing a camera to relocalise in a map built by a different camera. We also perform loop closing (detection + correction), at keyframerate, in loops containing hundreds of keyframes. For both relocalisation and loop closing, we propose a bag of words place recognizer with ORB features, which is able to recognize places spending less than 39 ms, including feature extraction, in databases containing 10K images (without geometrical verification). We evaluate the performance of this recognizer in four different datasets, achieving high recall and no false matches, and getting better results than the state-of-art in place recognition, being one order of magnitude faster.
Keywords
Computer scienceClosing (real estate)Artificial intelligenceComputer visionLoop (graph theory)Orb (optics)Feature extractionFrame (networking)Simultaneous localization and mappingRecall rateFeature (linguistics)Image (mathematics)RobotMobile robotMathematics
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Publication Info
- Year
- 2014
- Type
- article
- Citations
- 259
- Access
- Closed
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Cite This
Raul Mur-Artal,
Juan D. Tardós
(2014).
Fast relocalisation and loop closing in keyframe-based SLAM.
2014 IEEE International Conference on Robotics and Automation (ICRA)
.
https://doi.org/10.1109/icra.2014.6906953
Identifiers
- DOI
- 10.1109/icra.2014.6906953