Abstract
The traditional simultaneous localization and mapping (SLAM) system uses static points of the environment as features for real-time localization and mapping. When there are few available point features, the system is difficult to implement. A feasible solution is to introduce line features. In complex scenarios containing rich line segments, the description of line segments is not strongly differentiated, which can lead to incorrect association of line segment data, thus introducing errors into the system and aggravating the cumulative error of the system. To address this problem, a point-line stereo visual SLAM system incorporating semantic invariants is proposed in this paper. This system improves the accuracy of line feature matching by fusing line features with image semantic invariant information. When defining the error function, the semantic invariant is fused with the reprojection error function, and the semantic constraint is applied to reduce the cumulative error of the poses in the long-term tracking process. Experiments on the Office sequence of the TartanAir dataset and the KITTI dataset show that this system improves the matching accuracy of line features and suppresses the cumulative error of the SLAM system to some extent, and the mean relative pose error (RPE) is 1.38 and 0.0593 m, respectively.
Highlights
Received: 5 January 2021Since the introduction of Industry 4.0, the robot-led intelligent manufacturing industry has become the backbone of industrial development
This leads to the problem that after the introduction of line segments in simultaneous localization and mapping (SLAM) systems based on point-line features, the matching accuracy of line segments is low, which results in the accumulation of system errors
Based on the stereo point-line SLAM system, the present paper aims at the problem that after the introduction of line segments, the accuracy of data association is directly affected by the mismatching of line segments, which aggravates the cumulative error of the system
Summary
Since the introduction of Industry 4.0, the robot-led intelligent manufacturing industry has become the backbone of industrial development. The theory development related to line segments is not mature enough, mainly in the lack of accurate description of line segments, which can lead to wrong data association occurring in complicated scenes that include many line segments [27] This leads to the problem that after the introduction of line segments in SLAM systems based on point-line features, the matching accuracy of line segments is low, which results in the accumulation of system errors. We define the semantic reprojection error function of line segments and apply it to the pose optimization process to improve the robustness of data association In this way, the mid-term tracking of line segments is achieved, and the drift problem of trajectories is reduced. Around segment change dramatically, but its semantic description remains unchanged
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