PLI-VINS: Visual-Inertial SLAM Based on Point-Line Feature Fusion in Indoor Environment

In indoor low-texture environments, the point feature-based visual SLAM system has poor robustness and low trajectory accuracy. Therefore, we propose a visual inertial SLAM algorithm based on point-line feature fusion. Firstly, in order to improve the quality of the extracted line segment, a line segment extraction algorithm with adaptive threshold value is proposed. By constructing the adjacent matrix of the line segment and judging the direction of the line segment, it can decide whether to merge or eliminate other line segments. At the same time, geometric constraint line feature matching is considered to improve the efficiency of processing line features. Compared with the traditional algorithm, the processing efficiency of our proposed method is greatly improved. Then, point, line, and inertial data are effectively fused in a sliding window to achieve high-accuracy pose estimation. Finally, experiments on the EuRoC dataset show that the proposed PLI-VINS performs better than the traditional visual inertial SLAM system using point features and point line features.

Automated summary

This paper proposes a visual inertial SLAM algorithm based on point-line feature fusion, which improves the efficiency of the algorithm by improving line segment extraction and line feature matching, and achieves high-accuracy pose estimation by fusing point, line, and inertial data in a sliding window. Experiments show that the proposed algorithm performs better than traditional methods in handling SLAM tasks in indoor low-texture environments.