文章基本信息

标题：Visual SLAM in dynamic environments based on object detection
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作者：Yong-bao Ai ; Ting Rui ; Xiao-qiang Yang 等
期刊名称：Defence Technology
印刷版ISSN：2214-9147
出版年度：2021
卷号：17
期号：5
页码：1712-1721
DOI：10.1016/j.dt.2020.09.012
语种：English
出版社：Elsevier B.V.
摘要：AbstractA 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.
关键词：Visual SLAM;Object detection;Dynamic object probability model;Dynamic environments