Abstract：Visual simultaneous localization and mapping(SLAM) cannot be applied to dynamic environment. The mainstream solution is to combine segmentation network and SLAM. However, real-time operation of SLAM systems is not guaranteed due to the processing speed constraints of segmentation network. Therefore, a RGB-D SLAM algorithm based on delayed semantic information in dynamic environment is proposed. Firstly, tracking and segmentation threads run in parallel. To obtain the latest delayed semantic information, a cross-frame segmentation strategy is employed for image processing, and real-time semantic information for the current frame is generated by the tracking thread according to the delaysemantic information. Then, the dynamic point set of the current frame is selected and the real motion state of the prior dynamic object in the environment is determined by combining successful tracking count and epipolar constraints. When the object is determined as moving, the object area is further subdivided into rectangular grids, and dynamic feature points are removed with the grid as the minimum unit. Finally, the camera pose is tracked by static feature points and an environment map is constructed. Experiments on TUM RGB-D dynamic scene dataset and real scenes show that the proposed algorithm performs well and its effectiveness is verified.
王浩, 周申超, 方宝富. 动态环境下基于延迟语义的RGB-D SLAM算法[J]. 模式识别与人工智能, 2023, 36(10): 953-966.
WANG Hao, ZHOU Shenchao, FANG Baofu. RGB-D SLAM Algorithm Based on Delayed Semantic Information in Dynamic Environment. Pattern Recognition and Artificial Intelligence, 2023, 36(10): 953-966.
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