Visual SLAM algorithms rely on image sequences to achieve autonomous localization and mapping, where line features act as crucial structural information to enhance system robustness in weakly textured or structured environments. However, conventional line feature-based methods, such as the Line Segment Detector (LSD) algorithm, are prone to over-segmentation during line segment extraction, resulting in a large number of redundant short segments and fragmented line pieces. This phenomenon increases the false matching rate, which in turn degrades the accuracy of pose estimation and the overall stability of the Visual SLAM system. To address the above issues, we perform comparative experiments on multiple public datasets between the proposed improved line feature algorithm and classical counterparts from dimensions of time overhead, line feature number and detection accuracy. The results show that the proposed algorithm incurs a 20% increase in overall time for line feature extraction and matching, yet achieves a 14% higher proportion of long line segments, an 8% improvement in Average Precision (AP) and a 15% rise in Average Recall (AR). It is thus verified that the proposed method retains real-time performance while remarkably improving its line segment matching success rate, with its localization accuracy and system robustness maintained or even enhanced.
Guan et al. (Sat,) studied this question.