Title :
C-KLAM: Constrained keyframe-based localization and mapping
Author :
Nerurkar, Esha D. ; Wu, Kejian J. ; Roumeliotis, Stergios I.
Author_Institution :
Dept. of Comput. Sci. & Eng., Univ. of Minnesota, Minneapolis, MN, USA
fDate :
May 31 2014-June 7 2014
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.
Keywords :
SLAM (robots); matrix algebra; maximum likelihood estimation; C-KLAM approach; MAP estimator-based keyframe approach; MAP-based 3D SLAM; computational complexity; constrained keyframe-based localization and mapping; exteroceptive information; inertial measurement; information matrix; marginalization; maximum a posteriori estimation; proprioceptive information; simultaneous localization and mapping; visual measurement; Approximation methods; Cameras; Cost function; Jacobian matrices; Simultaneous localization and mapping; Trajectory;
Conference_Titel :
Robotics and Automation (ICRA), 2014 IEEE International Conference on
Conference_Location :
Hong Kong
DOI :
10.1109/ICRA.2014.6907385