C-KLAM: Constrained Keyframe Localization and Mapping for Long-Term Navigation

In this paper, we present C-KLAM, a Maximum A Posteriori (MAP) estimator-based keyframe approach for SLAM. As opposed to many existing keyframe-based SLAM approaches, that discard information from non-keyframes in order to reduce the computational complexity, the proposed C-KLAM presents a novel and computationally-efficient technique for incorporating most of this information, resulting in improved estimation accuracy. Specifically, C-KLAM projects information from the non-keyframes to the keyframes, using marginalization, while maintaining the sparse structure of the information matrix, to generate fast and efficient solutions. The performance of C-KLAM has been tested in both simulations and experimentally, 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.