LR-SLAM: Visual Inertial SLAM System with Redundant Line Feature Elimination

The present study focuses on the simultaneous localization and mapping (SLAM) system based on point and line features. Aiming to address the prevalent issue of repeated detection during line feature extraction in low-texture environments, a novel method for merging redundant line features is proposed. This method effectively mitigates the problem of increased initial pose estimation error that arises when the same line is erroneously detected as multiple lines in adjacent frames. Furthermore, recognizing the potential for the introduction of line features to prolong the marginalization process of the information matrix, optimization strategies are employed to accelerate this process. Additionally, to tackle the issue of insufficient point feature accuracy, subpixel technology is introduced to enhance the precision of point features, thereby further reducing errors. Experimental results on the European Robotics Challenge (EUROC) public dataset demonstrate that the proposed LR-SLAM system exhibits significant advantages over mainstream SLAM systems such as ORB-SLAM3, VINS-Mono, and PL-VIO in terms of accuracy, efficiency, and robustness.