English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In dynamic environments, achieving accurate and robust Visual SLAM (Simultaneous Localization and Mapping) remains a significant challenge, particularly for applications in robotic navigation and autonomous driving. This study introduces YG-SLAM, an innovative approach that integrates YOLOv8 and geometric constraints within the ORB-SLAM2 framework to adapt effectively to dynamic scenarios. YOLOv8 is employed for instance segmentation and dynamic object detection, enriching the semantic information while extracting image feature points. Geometric constraints, including epipolar geometry algorithms and Lucas-Kanade optical flow methods, are utilized to filter out dynamic objects effectively.The tracking thread exclusively relies on static feature points for camera pose estimation, substantially improving the system&#x2019;s localization accuracy. Experimental results on the TUM dataset demonstrate that YG-SLAM significantly outperforms traditional ORB-SLAM2 in dynamic environments. Specifically, the Root Mean Square Error (RMSE) of the absolute trajectory errors reduced by 96.51%, and the RMSE of the relative pose errors decreased by 93.60%, marking a significant advancement in the field of Visual SLAM.

YG-SLAM: Enhancing Visual SLAM in Dynamic Environments with YOLOv8 and Geometric Constraints