TY - GEN
T1 - DPF-SLAM
T2 - 2022 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2022
AU - Liu, Xin
AU - Wen, Shuhuan
AU - Yuan, Mingxing
AU - Li, Pengjiang
AU - Zhao, Yongjie
AU - Manfredi, Luigi
N1 - Funding Information:
Corresponding author: Shuhuan Wen [email protected] *The work is supported by the National Natural Science Foundation of China (No. 61773333, 62111530148), the China Scholarship Council (No. 201908130016) and the Hebei Province Graduate Innovation Funding Project (No. CXZZBS2022133). 1Engineering Research Center of the Ministry of Education for Intelligent Control System and Intelligent Equipment, and Key Laboratory of Industrial Computer Control Engineering of Hebei Province, Yanshan University, Qinhuangdao, China 2School of Artificial Intelligence, Nankai University, China 3Institute for Medical Science and Technology(IMSaT), University of Dundee, United Kingdom
Publisher Copyright:
© 2022 IEEE.
PY - 2022/9/5
Y1 - 2022/9/5
N2 - With the development of simultaneous localization and mapping (SLAM) technology, Dynamic SLAM has been a challenging research topic. This paper presents a dynamic probability fusion SLAM (DPF-SLAM) algorithm, which adds a semantic segmentation thread and a dense reconstruction thread to ORB-SLAM2. We integrate dynamic prior probability obtained by semantic segmentation with dynamic probability obtained by dynamic point detection, which can decrease the effect of dynamic objects on the localization accuracy and meanwhile achieve the dense reconstruction of a static background. We evaluate DPF-SLAM system on the public TUM RGBD dataset. The experimental results show that the proposed algorithm has better localization accuracy than DS-SLAM and ORB-SLAM2, which are two relatively new algorithms in this area, and can obtain a good dense reconstruction effect. Moreover, through the performance comparison with our previous work, it is found that the algorithm speed and positioning accuracy are improved.
AB - With the development of simultaneous localization and mapping (SLAM) technology, Dynamic SLAM has been a challenging research topic. This paper presents a dynamic probability fusion SLAM (DPF-SLAM) algorithm, which adds a semantic segmentation thread and a dense reconstruction thread to ORB-SLAM2. We integrate dynamic prior probability obtained by semantic segmentation with dynamic probability obtained by dynamic point detection, which can decrease the effect of dynamic objects on the localization accuracy and meanwhile achieve the dense reconstruction of a static background. We evaluate DPF-SLAM system on the public TUM RGBD dataset. The experimental results show that the proposed algorithm has better localization accuracy than DS-SLAM and ORB-SLAM2, which are two relatively new algorithms in this area, and can obtain a good dense reconstruction effect. Moreover, through the performance comparison with our previous work, it is found that the algorithm speed and positioning accuracy are improved.
UR - http://www.scopus.com/inward/record.url?scp=85138743160&partnerID=8YFLogxK
U2 - 10.1109/RCAR54675.2022.9872255
DO - 10.1109/RCAR54675.2022.9872255
M3 - Conference contribution
AN - SCOPUS:85138743160
T3 - 2022 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2022
SP - 360
EP - 365
BT - 2022 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2022
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 17 July 2022 through 22 July 2022
ER -