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

• Published in: Remote sensing (Basel, Switzerland) Vol. 15; no. 7; p. 1893
• Main Authors: Yuan, Chaofeng, Xu, Yuelei, Zhou, Qing
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.04.2023
• Subjects: Accuracy; Algorithms; Artificial intelligence; Bayesian analysis; Cameras; Comparative analysis; Digital mapping; Dynamic stability; feature detection; Geometric constraints; Geometry; line and point features; Localization; Machine learning; Machine vision; Mapping; Matching; Methods; Object recognition; Optimization; Remote sensing; Semantic segmentation; Semantics; Simultaneous localization and mapping; Static objects; Tracking; visual SLAM; Visual stimuli; China
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs15071893

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.