PLDS-SLAM: Point and Line Features SLAM in Dynamic Environment

被引:33
作者
Yuan, Chaofeng [1 ]
Xu, Yuelei [1 ]
Zhou, Qing [1 ]
机构
[1] Northwestern Polytech Univ, Unmanned Syst Res Inst, Xian 710072, Peoples R China
关键词
visual SLAM; line and point features; feature detection;
D O I
10.3390/rs15071893
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Visual simultaneous localization and mapping (SLAM), based on point features, achieves high localization accuracy and map construction. They primarily perform simultaneous localization and mapping based on static features. Despite their efficiency and high precision, they are prone to instability and even failure in complex environments. In a dynamic environment, it is easy to keep track of failures and even failures in work. The dynamic object elimination method, based on semantic segmentation, often recognizes dynamic objects and static objects without distinction. If there are many semantic segmentation objects or the distribution of segmentation objects is uneven in the camera view, this may result in feature offset and deficiency for map matching and motion tracking, which will lead to problems, such as reduced system accuracy, tracking failure, and track loss. To address these issues, we propose a novel point-line SLAM system based on dynamic environments. The method we propose obtains the prior dynamic region features by detecting and segmenting the dynamic region. It realizes the separation of dynamic and static objects by proposing a geometric constraint method for matching line segments, combined with the epipolar constraint method of feature points. Additionally, a dynamic feature tracking method based on Bayesian theory is proposed to eliminate the dynamic noise of points and lines and improve the robustness and accuracy of the SLAM system. We have performed extensive experiments on the KITTI and HPatches datasets to verify these claims. The experimental results show that our proposed method has excellent performance in dynamic and complex scenes.
引用
收藏
页数:19
相关论文
共 33 条
[31]   UnrollingNet: An attention-based deep learning approach for the segmentation of large-scale point clouds of tunnels [J].
Zhang, Zhaoxiang ;
Ji, Ankang ;
Wang, Kunyu ;
Zhang, Limao .
AUTOMATION IN CONSTRUCTION, 2022, 142
[32]   Unsupervised Domain Adaptation of High-Resolution Aerial Images via Correlation Alignment and Self Training [J].
Zhang, Zhaoxiang ;
Doi, Kento ;
Iwasaki, Akira ;
Xu, Guodong .
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2021, 18 (04) :746-750
[33]  
Zuo XX, 2017, IEEE INT C INT ROBOT, P1775, DOI 10.1109/IROS.2017.8205991