VPL-SLAM: A Vertical Line Supported Point Line Monocular SLAM System
Qi Chen, Yu Cao, Jiawei Hou, Guanghao Li, Shoumeng Qiu, Bo Chen, Xiangyang Xue, Hong Lu, Jian Pu
Abstract
Traditional monocular visual simultaneous localization and mapping (SLAM) systems rely on point features or line features to estimate and optimize the camera trajectory and build a map of the surrounding environment. However, in complex scenarios such as underground parking, the performance of traditional point-line SLAM systems tends to degrade due to mirror reflection, illumination change, poor texture, and other interference. This paper proposes VPL-SLAM, a structural vertical line supported point-line monocular SLAM system that works well in complex environments such as underground parking or campus. The proposed system leverages structural vertical lines at all instances of the process. With the assistance of the structural vertical lines and global vertical direction, our system can output a more accurate visual odometry result. Furthermore, the resulting map of our system is a more reasonable structural line feature map than the previous point-line-based monocular SLAM systems. Our system has been tested with the popular autonomous driving dataset Kitti Odometry. In addition, to fully test the proposed SLAM system, we also test our system using a self-collected dataset, including underground parking and campus scenarios. As a result, our proposal reveals a more accurate navigation result and a more reasonable structural resulting map compared to state-of-the-art point-line SLAM systems such as Structure PLP-SLAM.