The Journal of China Universities of Posts and Telecommunications ›› 2023, Vol. 30 ›› Issue (3): 14-24.doi: 10.19682/j.cnki.1005-8885.2022.1011

• Artificial intelligence • Previous Articles     Next Articles

DRO-SLAM: Real-time object-aware SLAM for navigation robots and autonomous driving in dynamic environments

Wang Zixian, Zhang Miao, Yan Danfeng    

  1. School of Computer Science, Beijing University of Posts and Telecommunications, Beijing 100876, China
  • Received:2021-10-27 Revised:2022-04-07 Online:2023-06-30 Published:2023-06-30
  • Contact: Yan Danfeng

Abstract: Traditional simultaneous localization and mapping ( SLAM) mostly performs under the assumption of an ideal static environment, which is not suitable for dynamic environments in the real world. Dynamic real-time object-aware SLAM ( DRO-SLAM) is proposed in this paper, which is a visual SLAM that can realize simultaneous localizing and mapping and tracking of moving objects indoor and outdoor at the same time. It can use target recognition, oriented fast and rotated brief (ORB) feature points, and optical flow assistance to track multi-target dynamic objects and remove them during dense point cloud reconstruction while estimating their pose. By verifying the algorithm effect on the public dataset and comparing it with other methods, it can be obtained that the proposed algorithm has certain guarantees in real-time and accuracy, it also provides more functions. DRO-SLAM can provide the solution to automatic navigation which can realize lightweight deployment, provide more vehicles, pedestrians and other environmental information for navigation.

Key words: simultaneous localization and mapping (SLAM), object tracking, stereo vision, dynamic environment, data association

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