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Real-time large-scale dense mapping with surfels
Fu XY(付兴银)1,2,3,4; Zhu F(朱枫)1,3,4; Wu QX(吴清潇)1,3,4; Sun YL(孙云雷)1,2,3,4; Lu RR(鲁荣荣)1,2,3,4; Yang, Ruigang5,6
Source PublicationSensors (Switzerland)
Indexed BySCI ; EI
EI Accession number20182005198454
WOS IDWOS:000435580300192
Contribution Rank1
Funding OrganizationNSFC ; State Key Laboratory of Robotics
KeywordDense Mapping Rgb-d Camera Surfel Loop Closure Embedded Deformation Graph

Real-time dense mapping systems have been developed since the birth of consumer RGB-D cameras. Currently, there are two commonly used models in dense mapping systems: truncated signed distance function (TSDF) and surfel. The state-of-the-art dense mapping systems usually work fine with small-sized regions. The generated dense surface may be unsatisfactory around the loop closures when the system tracking drift grows large. In addition, the efficiency of the system with surfel model slows down when the number of the model points in the map becomes large. In this paper, we propose to use two maps in the dense mapping system. The RGB-D images are integrated into a local surfel map. The old surfels that reconstructed in former times and far away from the camera frustum are moved from the local map to the global map. The updated surfels in the local map when every frame arrives are kept bounded. Therefore, in our system, the scene that can be reconstructed is very large, and the frame rate of our system remains high. We detect loop closures and optimize the pose graph to distribute system tracking drift. The positions and normals of the surfels in the map are also corrected using an embedded deformation graph so that they are consistent with the updated poses. In order to deal with large surface deformations, we propose a new method for constructing constraints with system trajectories and loop closure keyframes. The proposed new method stabilizes large-scale surface deformation. Experimental results show that our novel system behaves better than the prior state-of-the-art dense mapping systems.

WOS SubjectChemistry, Analytical ; Electrochemistry ; Instruments & Instrumentation
WOS Research AreaChemistry ; Electrochemistry ; Instruments & Instrumentation
Funding ProjectNSFC[U1713216] ; State Key Laboratory of Robotics[2017-Z21]
Citation statistics
Cited Times:3[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Corresponding AuthorFu XY(付兴银)
Affiliation1.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China;
2.University of Chinese Academy of Sciences, Beijing 100049, China;
3.Key Laboratory of Opto-Electronic Information Processing, CAS, Shenyang 110016, China;
4.Key Lab of Image Understanding and Computer Vision, Shenyang 110016, China;
5.Baidu Inc., Beijing 100193, China;
6.National Engineering Laboratory of Deep Learning Technology and Application, Beijing 100193, China
Recommended Citation
GB/T 7714
Fu XY,Zhu F,Wu QX,et al. Real-time large-scale dense mapping with surfels[J]. Sensors (Switzerland),2018,18(5):1-19.
APA Fu XY,Zhu F,Wu QX,Sun YL,Lu RR,&Yang, Ruigang.(2018).Real-time large-scale dense mapping with surfels.Sensors (Switzerland),18(5),1-19.
MLA Fu XY,et al."Real-time large-scale dense mapping with surfels".Sensors (Switzerland) 18.5(2018):1-19.
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