TDO-SLAM: Traffic Sign and Dynamic Object Based Visual SLAM

This paper introduces a real-time visual SLAM system, TDO-SLAM, using only a stereo vision camera. TDO-SLAM works not only in static but also in dynamic road environment by incorporating the object motion and the planar property of standing traffic signs. Traditional visual SLAM systems assume that...

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Bibliographic Details
Published in:IEEE access 2024, Vol.12, p.24569-24582
Main Authors: Park, Soon-Yong, Lee, Junesuk
Format: Article
Language:English
Subjects:
autonomous vehicle
Bundle adjustment
Cameras
Datasets
Dynamic SLAM
Dynamics
Feature extraction
Object motion
Pose estimation
Roads
Simultaneous localization and mapping
Traffic control
Traffic signs
Urban environments
Vehicle dynamics
visual localization
Visualization
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Summary:This paper introduces a real-time visual SLAM system, TDO-SLAM, using only a stereo vision camera. TDO-SLAM works not only in static but also in dynamic road environment by incorporating the object motion and the planar property of standing traffic signs. Traditional visual SLAM systems assume that the road environment is static. However, a variety of dynamic objects exist in the real-world urban environment. Thus, the traditional SLAM systems are subject to fail due to the various motion of the dynamic objects. To solve this inherent problem in the dynamic environment, TDO-SLAM detects, tracks, and manages the global object identification of dynamic objects and standing traffic signs through a novel Object-Level-Tracking method. We improve the accuracy of camera pose estimation through several steps of bundle adjustments, including the residual terms for the planar constraint of traffic signs and the dynamic object motion. Experimental results show that pose estimation accuracy is improved in complex environment with several dynamic objects and traffic signs. Performance of TDO-SLAM is analyzed and compared with ORB-SLAM2, ORB-SLAM3, and DynaSLAM using three benchmark datasets, KITTI Odometry dataset, KITTI Raw dataset, and Complex Urban dataset.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2024.3362675