Enhancing Robustness of Line Tracking Through Semi-Dense Epipolar Search in Line-Based SLAM

• Published in: 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) pp. 3483 - 3490
• Main Authors: Seo, Dong-Uk, Lim, Hyungtae, Lee, Eungchang Mason, Lim, Hyunjun, Myung, Hyun
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.10.2023
• Subjects: Maintenance engineering; Measurement uncertainty; Robustness; Simultaneous localization and mapping; Three-dimensional displays; Uncertainty; Visualization
• ISSN: 2153-0866
• DOI: 10.1109/IROS55552.2023.10342497

Line information from urban structures can be exploited as an additional geometrical feature to achieve robust vision-based simultaneous localization and mapping (SLAM) systems in textureless scenes. Sometimes, however, conventional line tracking methods fail to track caused by image blur or occlusion. Even though these lost line features are just a subset of plenty of features, the failure in feature tracking can potentially lead to performance degradation of the SLAM system, particularly in textureless environments. To tackle this problem, we propose a robust line-tracking method for line-based monocular visual-inertial odometry. The proposed method generates a semi-dense map composed of depth and sparsity mesh using estimated 3D features. By leveraging the semi-dense map, our method performs a range-adaptive epipo-lar search to match the lines, allowing for robust line tracking while simultaneously reducing false positives. Furthermore, an algorithm to avoid conflicts is proposed, which occurs when the tracked lines from consecutive matching do not accord with the lines matched by our method. This algorithm discriminately maintains line features while appropriately aggregating lines spread across multiple frames. As evaluated in the EuRoC dataset and a more challenging textureless corridor scene, our proposed method shows substantial performance increases compared with other line-based visual (-inertial) approaches.