TY - GEN
T1 - DRG-SLAM
T2 - 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2022
AU - Wang, Yanan
AU - Xu, Kun
AU - Tian, Yaobin
AU - Ding, Xilun
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Visual SLAM methods based on point features have achieved acceptable results in texture-rich static scenes, but they often suffer from a deficiency of texture and the existence of dynamic objects in real indoor scenes, which limits the application of these methods. In this paper, we have presented DRG-SLAM, which combines line features and plane features into point features to improve the robustness of the system. We tested the proposed algorithm on publicly available datasets, and the results demonstrate that the algorithm has superior accuracy and robustness in indoor dynamic scenes compared with the state-of-the-art methods.
AB - Visual SLAM methods based on point features have achieved acceptable results in texture-rich static scenes, but they often suffer from a deficiency of texture and the existence of dynamic objects in real indoor scenes, which limits the application of these methods. In this paper, we have presented DRG-SLAM, which combines line features and plane features into point features to improve the robustness of the system. We tested the proposed algorithm on publicly available datasets, and the results demonstrate that the algorithm has superior accuracy and robustness in indoor dynamic scenes compared with the state-of-the-art methods.
UR - https://www.scopus.com/pages/publications/85146341158
U2 - 10.1109/IROS47612.2022.9981238
DO - 10.1109/IROS47612.2022.9981238
M3 - 会议稿件
AN - SCOPUS:85146341158
T3 - IEEE International Conference on Intelligent Robots and Systems
SP - 1352
EP - 1359
BT - 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2022
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 23 October 2022 through 27 October 2022
ER -