Scalable Monocular SLAM by Fusing and Connecting Line Segments with Inverse Depth Filter

Abstract

In this paper we propose a fast and robust line-based approach to monocular SLAM. It relies on a novel inverse depth representation of lines capable of tracking line segments in long image consequences. Tracked lines through frames provide crucial directional and positional knowledge for boosting localization performance, and they are more informative in charactering environments than points especially for urban outdoor and indoor scenes. The developed two-parameter inverse depth representation of lines is applicable for Kalman filter to achieve an efficient solver due to its linearity, which has lower computational cost compared to binary descriptors. This filter is also harmonious with inverse depth filter of points, both of which are incorporated under a unified minimization framework to enhance the performance of monocular SLAM. Real world monocular sequences have demonstrated that the proposed SLAM system outperforms the state-of-the-art and produces accurate results in both indoor and outdoor scenes.