计算机视觉
同时定位和映射
惯性参考系
人工智能
计算机科学
可视化
惯性测量装置
机器人
移动机器人
物理
量子力学
作者
Shuhuan Wen,Sheng Tao,Xin Liu,Artur Babiarz,F. Richard Yu
出处
期刊:IEEE Transactions on Instrumentation and Measurement
[Institute of Electrical and Electronics Engineers]
日期:2024-01-01
卷期号:73: 1-8
被引量:2
标识
DOI:10.1109/tim.2024.3396858
摘要
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 existing algorithms in terms of accuracy and robustness.
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