TY - GEN
T1 - Research on RGB-D Visual SLAM Algorithm Based on Adaptive Target Detection
AU - Wei, Baoguo
AU - Zhao, Lina
AU - Li, Lixin
AU - Li, Xu
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Most of the traditional SLAM systems are based on static environment assumptions, but they are easily affected by dynamic targets in real environments, resulting in a serious degradation of the robustness and accuracy of the algorithms. In this paper, we focus on visual SLAM systems in dynamic scenes, introducing an object detection network in SLAM to obtain the low-level semantic information of dynamic targets, and adopting a new dynamic point selection strategy to classify the detected targets into three motion types, and then fusing the semantic information to eliminate the dynamic feature points. Experiments show that the proposed method outperforms traditional methods in dynamic scenarios, and the real-time performance of the proposed method is improved compared with the semantic segmentation-based SLAM system.
AB - Most of the traditional SLAM systems are based on static environment assumptions, but they are easily affected by dynamic targets in real environments, resulting in a serious degradation of the robustness and accuracy of the algorithms. In this paper, we focus on visual SLAM systems in dynamic scenes, introducing an object detection network in SLAM to obtain the low-level semantic information of dynamic targets, and adopting a new dynamic point selection strategy to classify the detected targets into three motion types, and then fusing the semantic information to eliminate the dynamic feature points. Experiments show that the proposed method outperforms traditional methods in dynamic scenarios, and the real-time performance of the proposed method is improved compared with the semantic segmentation-based SLAM system.
KW - Dynamic SLAM
KW - Object detection
KW - ORBSLAM
UR - http://www.scopus.com/inward/record.url?scp=85184854565&partnerID=8YFLogxK
U2 - 10.1109/ICSPCC59353.2023.10400307
DO - 10.1109/ICSPCC59353.2023.10400307
M3 - 会议稿件
AN - SCOPUS:85184854565
T3 - Proceedings of 2023 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2023
BT - Proceedings of 2023 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2023
Y2 - 14 November 2023 through 17 November 2023
ER -