LAP-SLAM: A Line-Assisted Point-Based Monocular VSLAM

While the performance of the state-of-the-art point-based VSLAM (vision simultaneous localization and mapping) systems in well textured sequences is impressive, their performance in poorly textured situations is not satisfactory enough. A sensible alternative or addition is to consider lines. In this paper, we propose a novel line-assisted point-based VSLAM algorithm (LAP-SLAM). Our algorithm uses lines without descriptor matching, and the lines are used to assist the computation conducted by points. To the best of our knowledge, this paper proposes a new way to include line information in VSLAM. The basic idea is to use the collinear relationship of points to optimize the current point-based VSLAM algorithm. In LAP-SLAM, we propose a practical algorithm to match lines and compute the collinear relationship of points, a line-assisted bundle adjustment approach and a modified perspective-n-point (PnP) approach. We built our system based on the architecture and pipeline of ORB-SLAM. We evaluate the proposed method on a diverse range of indoor sequences in the TUM dataset and compare with point-based and point-line-based methods. The results show that the accuracy of our algorithm is close to point-line-based VSLAM systems with a much faster speed.