GNSS World of China

Volume 47 Issue 5
Nov.  2022
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GAO Yi, WANG Qing, YANG Gaochao, LIU Pengfei. Indoor dynamic SLAM based on geometric constraints and target detection[J]. GNSS World of China, 2022, 47(5): 51-56. doi: 10.12265/j.gnss.2022099
Citation: GAO Yi, WANG Qing, YANG Gaochao, LIU Pengfei. Indoor dynamic SLAM based on geometric constraints and target detection[J]. GNSS World of China, 2022, 47(5): 51-56. doi: 10.12265/j.gnss.2022099
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Indoor dynamic SLAM based on geometric constraints and target detection

doi: 10.12265/j.gnss.2022099

  • College of Instrument Science and Engineering, Southeast University, Nanjing 210096, China

  • Received Date: 2022-06-06
  • Accepted Date: 2022-06-06
    • Available Online: 2022-10-28
    Abstract
  • Aiming at the problem of low localization accuracy and poor map effect of visual simultaneous localization and mapping (SLAM) in indoor dynamic environment, a indoor dynamic SLAM method is proposed based on geometric constraints and target detection. The target detection network is used to obtain semantic information and a method for missing detection of moving objects is proposed. Based on prior knowledge, an information determination method is proposed to accurately identify dynamic regions. Dynamic points are eliminated based on geometric constraints and deep learning. Static points are used to estimate camera pose. A closed-loop static map is builded based on the stored information. The experiment on TUM dataset shows that the localization accuracy is 97.5% higher than that of ORB-SLAM2 and the performance is better than other dynamic SLAM. The experiment in the indoor real environment shows that the static map is more accurate. The localization accuracy and the map effect of indoor dynamic SLAM are improved effectively.

     

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