التفاصيل البيبلوغرافية
| العنوان: |
Visual SLAM in dynamic environments based on object detection. |
| المؤلفون: |
Yong-bao Ai, Ting Rui, Xiao-qiang Yang, Jia-lin He, Lei Fu, Jian-bin Li, Ming Lu |
| المصدر: |
Defence Technology; Oct2021, Vol. 17 Issue 5, p1712-1721, 10p |
| مصطلحات موضوعية: |
DEEP learning, OBJECT recognition (Computer vision), PROBABILITY theory, STABILITY (Mechanics), MACHINE learning |
| مستخلص: |
A great number of visual simultaneous localization and mapping (VSLAM) systems need to assume static features in the environment. However, moving objects can vastly impair the performance of a VSLAM system which relies on the static-world assumption. To cope with this challenging topic, a real-time and robust VSLAM system based on ORB-SLAM2 for dynamic environments was proposed. To reduce the influence of dynamic content, we incorporate the deep-learning-based object detection method in the visual odometry, then the dynamic object probability model is added to raise the efficiency of object detection deep neural network and enhance the real-time performance of our system. Experiment with both on the TUM and KITTI benchmark dataset, as well as in a real-world environment, the results clarify that our method can significantly reduce the tracking error or drift, enhance the robustness, accuracy and stability of the VSLAM system in dynamic scenes. [ABSTRACT FROM AUTHOR] |
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