GGC-SLAM: A VSLAM System Based on Predicted Static Probability of Feature Points in Dynamic Environments

作者：Qian Sun, Wa Liu, Junjing Zou, Ziqiang Xu, Yibing Li · 发表于：Research Square · 年份：2024 · DOI：10.21203/rs.3.rs-4463656/v1 · 被引用次数：1 · 研究领域：Robotics and Sensor-Based Localization、Advanced Image and Video Retrieval Techniques、Robotic Path Planning Algorithms

Abstract Most existing Visual Simultaneous Localization and Mapping (VSLAM) systems heavily rely on the assumption of a static world. However, in real-world scenarios, moving objects often reduce the accuracy and robustness of these systems. To mitigate the impact of moving objects on SLAM system performance, this paper introduces GGC-SLAM, a VSLAM system designed for dynamic environments based on the static probability of predictive feature points. Building on the foundation of ORB-SLAM2, this system incorporates a lightweight object detection thread for acquiring semantic information. It also integrates the Grid-based Motion Statistics (GMS) and Graph-Cut RANdom SAmple Consensus (GC-RANSAC) algorithms to enhance the speed and precision of fundamental matrix computation. The system preliminarily predicts the static probability of feature points by combining semantic information with epipolar constraints, then refines the differentiation between dynamic and static feature points using the initial probability information and a Conditional Random Field (CRF), ultimately excluding feature points deemed dynamic. We conducted evaluations using the TUM public dataset and in real-world environments, and the results show that GGC-SLAM can effectively handle dynamic feature points in dynamic scenes. While ensuring real-time operation, it demonstrates more accurate localization compared to other advanced dynamic SLAM systems. Particularly in high-dynamic scenes, our system's average a...