C-KLAM: Constrained keyframe-based localization and mapping

Esha D. Nerurkar, Kejian J. Wu, Stergios I. Roumeliotis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

41 Scopus citations


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 languageEnglish (US)
Title of host publicationProceedings - IEEE International Conference on Robotics and Automation
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781479936854, 9781479936854
StatePublished - Sep 22 2014
Event2014 IEEE International Conference on Robotics and Automation, ICRA 2014 - Hong Kong, China
Duration: May 31 2014Jun 7 2014

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
ISSN (Print)1050-4729


Other2014 IEEE International Conference on Robotics and Automation, ICRA 2014
CityHong Kong

Bibliographical note

Publisher Copyright:
© 2014 IEEE.


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