Hybrid Dynamic Point Removal and Ellipsoid Modelling of Object-Based Semantic SLAM

Abstract

For the issue of low positioning accuracy in dynamic environments with traditional simultaneous localisation and mapping (SLAM), a dynamic point removal strategy combining object detection and optical flow tracking has been proposed. To fully utilise the semantic information, an ellipsoid model of the detected semantic objects was first constructed based on the plane and point cloud constraints, which assists in loop closure detection. Bilateral semantic map matching was achieved through the Kuhn–Munkres (KM) algorithm maximum weight assignment, and the pose transformation between local and global maps was determined by the random sample consensus (RANSAC) algorithm. Finally, a stable semantic SLAM system suitable for dynamic environments was constructed. The effectiveness of achieving the system's positioning accuracy under dynamic interference and large visual-inertial loop closure was verified by the experiment.