Bayesian filtering for keyframe-based visual SLAM

Abstract

Keyframe-based camera tracking methods can reduce error accumulation in that they reduce the number of camera poses to be estimated by selecting a set of keyframes from an image sequence. In this paper, we propose a novel Bayesian filtering framework for keyframe-based camera tracking and 3D mapping. Our Bayesian filtering enables an effective estimation of keyframe poses using all measurements obtained at non-keyframe locations, which improves the accuracy of the estimated path. In addition, we discuss the independence problem between the process noise and the measurement noise when employing vision-based motion estimation approaches for the process model, and we present a method of ensuring independence by dividing the measurements obtained from a single sensor into two sets which are exclusively used for the process and measurement models. We demonstrate the performance of the proposed approach in terms of the consistency of the global map and the accuracy of the estimated path.