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)|
|Number of pages||6|
|Journal||Proceedings - IEEE International Conference on Robotics and Automation|
|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