Capping computation time and storage requirements for appearance-based localization with CAT-SLAM

Maddern, William, Milford, Michael, & Wyeth, Gordon (2012) Capping computation time and storage requirements for appearance-based localization with CAT-SLAM. In Papanikolopoulos, Nikos (Ed.) Proceedings of the 2012 IEEE International Conference on Robotics and Automation, IEEE, St. paul, Minn., pp. 822-827.

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Appearance-based localization is increasingly used for loop closure detection in metric SLAM systems. Since it relies only upon the appearance-based similarity between images from two locations, it can perform loop closure regardless of accumulated metric error. However, the computation time and memory requirements of current appearance-based methods scale linearly not only with the size of the environment but also with the operation time of the platform. These properties impose severe restrictions on longterm autonomy for mobile robots, as loop closure performance will inevitably degrade with increased operation time. We present a set of improvements to the appearance-based SLAM algorithm CAT-SLAM to constrain computation scaling and memory usage with minimal degradation in performance over time. The appearance-based comparison stage is accelerated by exploiting properties of the particle observation update, and nodes in the continuous trajectory map are removed according to minimal information loss criteria. We demonstrate constant time and space loop closure detection in a large urban environment with recall performance exceeding FAB-MAP by a factor of 3 at 100% precision, and investigate the minimum computational and memory requirements for maintaining mapping performance.

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8 citations in Scopus
4 citations in Web of Science®
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ID Code: 51540
Item Type: Conference Paper
Refereed: Yes
Keywords: Equations, Mathematical model, Memory management, Particle filters, Simultaneous localization and mapping , Visualization , Trajectory
DOI: 10.1109/ICRA.2012.6224622
ISBN: 9781467314039
Subjects: Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > ELECTRICAL AND ELECTRONIC ENGINEERING (090600) > Control Systems Robotics and Automation (090602)
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: © 2012 IEEE
Deposited On: 11 Jul 2012 02:20
Last Modified: 12 Jun 2013 14:55

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