Abstract
In this paper, we present C-KLAM, a Maximum A Posteriori (MAP) estimator-based keyframe approach for SLAM. Instead of discarding information from non-keyframes for reducing the computational complexity, the proposed C-KLAM presents a novel, elegant, and computationally-efficient technique for incorporating most of this information in a consistent manner, resulting in improved estimation accuracy. To achieve this, C-KLAM projects both proprioceptive and exteroceptive information from the non-keyframes to the keyframes, using marginalization, while maintaining the sparse structure of the associated information matrix, resulting in fast and efficient solutions. The performance of C-KLAM has been tested in experiments, using visual and inertial measurements, to demonstrate that it achieves performance comparable to that of the computationally-intensive batch MAP-based 3D SLAM, that uses all available measurement information.
Original language | English (US) |
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Title of host publication | Proceedings - IEEE International Conference on Robotics and Automation |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 3638-3643 |
Number of pages | 6 |
ISBN (Electronic) | 9781479936854, 9781479936854 |
DOIs | |
State | Published - Sep 22 2014 |
Event | 2014 IEEE International Conference on Robotics and Automation, ICRA 2014 - Hong Kong, China Duration: May 31 2014 → Jun 7 2014 |
Publication series
Name | Proceedings - IEEE International Conference on Robotics and Automation |
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ISSN (Print) | 1050-4729 |
Other
Other | 2014 IEEE International Conference on Robotics and Automation, ICRA 2014 |
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Country/Territory | China |
City | Hong Kong |
Period | 5/31/14 → 6/7/14 |
Bibliographical note
Publisher Copyright:© 2014 IEEE.