CD-SLAM: A Real-Time Stereo Visual–Inertial SLAM for Complex Dynamic Environments With Semantic and Geometric Information

The most commonly used simultaneous localization and mapping (SLAM) scheme often assumes a static environment, leading to significant errors in pose estimation when operating in highly dynamic scenes. To address this limitation and improve the robustness and accuracy of positioning in dynamic environments, this study proposes CD-SLAM, a real-time stereo vision inertial SLAM system specifically designed for complex dynamic environments, based on ORB-SLAM3. CD-SLAM enhances the tracking thread and introduces a new parallel thread that utilizes YOLOv5 to detect objects in each input frame and extract semantic information. This semantic information, combined with prior information from the inertial measurement unit (IMU), is used for pose estimation, eliminating the pose information of dynamic objects and consequently improving the accuracy and robustness of positioning. Furthermore, CD-SLAM employs scene flow to calculate the distance between adjacent frames and determine the spatial velocity between them, compensating for potential static information through a velocity filtering algorithm. To enhance positioning accuracy in challenging environments with weak textures, CD-SLAM integrates an IMU for motion prediction and coherence detection. Finally, appeal information is integrated to determine the motion status of objects in the scene and filter out dynamic feature points. Experimental tests conducted on the VIODE dataset demonstrate that CD-SLAM outperforms the existing algorithms in terms of accuracy and robustness.