Visual SLAM in dynamic environments based on object detection |
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Authors: | Yong-bao Ai Ting Rui Xiao-qiang Yang Jia-lin He Lei Fu Jian-bin Li Ming Lu |
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Affiliation: | College of Field Engineering,People's Liberation Army Engineering University,Nanjing,210007,China;JinKen College of Technology,Nanjing,211156,China;Research Institute of Chemical Defense,Academy of Military Sciences,Beijing,102205,China |
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Abstract: | 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. |
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Keywords: | Visual SLAM Object detection Dynamic object probability model Dynamic environments |
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