PLS-FUSION: Tightly-Coupled Stereo Visual-Inertial SLAM With Point and Line Features

In this paper, we propose the PLS-FUSION, where P stands for point features, L for line features, and S for stereo vision. Together, these components form a tightly-coupled stereo visual-inertial Simultaneous Localization and Mapping (SLAM) system that leverages both point and line features to enhance the robustness and accuracy of tracking. Compared with the classic SLAM methods which only use the point features, we extract both the point features and the line features of the stereo camera in the front end and use a modified Line Segment Detector (LSD) algorithm of PL-VINS to improve the speed of extracting the line features. We use Plücker coordinates and orthonormal representation to represent the line features and add re-projection errors of the line features between the stereo camera in the back end. Our proposal has been tested with several popular datasets (EuRoC and TUMVI) and in the real world with our flight platform based on PX4 and QGroundControl (QGC). The experiments validate that PLS-FUSION has a more robust performance than state-of-the-art methods such as PL-VINS and VINS-FUSION.