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Jiang, H, Cang, N, Lin, Y, Guo, D and Zhang, W (2024) LR-SLAM: Visual Inertial SLAM System with Redundant Line Feature Elimination. Journal of Intelligent and Robotic Systems, 110 (4). ISSN 0921-0296
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LR-SLAM Visual Inertial SLAM System with Redundant Line Feature Elimination.pdf - Published Version Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (1MB) | Preview |
Abstract
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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | 4605 Data Management and Data Science; 46 Information and Computing Sciences; 40 Engineering; 0801 Artificial Intelligence and Image Processing; 1702 Cognitive Sciences; Industrial Engineering & Automation; 4007 Control engineering, mechatronics and robotics; 4602 Artificial intelligence |
| Subjects: | T Technology > T Technology (General) T Technology > TA Engineering (General). Civil engineering (General) |
| Divisions: | Engineering |
| Publisher: | Springer |
| SWORD Depositor: | A Symplectic |
| Date Deposited: | 25 Mar 2025 17:30 |
| Last Modified: | 25 Mar 2025 17:30 |
| DOI or ID number: | 10.1007/s10846-024-02184-2 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/26003 |
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