Real-time monocular visual-inertial SLAM with structural constraints of line and point-line fusion

In order to solve the problem of poor performance of traditional point feature algorithm under low texture and poor illumination, this paper presents a new visual SLAM method based on point-line fusion of line structure constraint. This method first uses an algorithm for homogeneity to process the extracted point features, solving the traditional problem of excessive aggregation and overlap of corner points, which makes the visual front end better able to obtain environmental information. In addition, improved line extraction method algorithm by using the strategy of eliminating the line length makes the line extraction performance twice as efficient as the LSD algorithm, the optical flow tracking algorithm is used to replace the traditional matching algorithm to reduce the running time of the system. In particular, the paper proposes a new constraint on the position of the spatially extracted lines, using the parallelism of 3D lines to correct for degraded lines in the projection process, and adding a new constraint on the line structure to the error function of the whole system, the newly constructed error function is optimized by sliding window, which significantly improves the accuracy and completeness of the whole system in constructing maps. Finally, the performance of the algorithm was tested on a publicly available dataset. The experimental results show that our algorithm performs well in point extraction and matching, the proposed point-line fusion system is better than the popular VINS-mono and PL-VINS algorithms in terms of running time, quality of information obtained, and positioning accuracy.

Automated summary

In this paper, a new visual SLAM method based on point-line fusion of line structure constraint is proposed to solve the problem of poor performance of traditional point feature algorithm under low texture and poor illumination. By improving the point feature processing and line extraction methods, as well as introducing parallelism constraint and optimizing the error function, the accuracy and completeness of map construction are significantly improved.