Sparse point cloud registration and aggregation with mesh‐based generalized iterative closest point

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dc.contributor.authorYoung M
dc.contributor.authorMcCulloch J
dc.contributor.authorGreen R
dc.contributor.authorPretty, Christopher
dc.date.accessioned2021-11-01T22:11:42Z
dc.date.available2021-11-01T22:11:42Z
dc.date.issued2021en
dc.date.updated2021-08-31T20:54:10Z
dc.description.abstractAccurate registration is critical for robotic mapping and simultaneous localization and mapping (SLAM). Sparse or non-uniform point clouds can be very challenging to register, even in ideal environments. Previous research by Holz et al. has developed a mesh-based extension to the popular generalized iterative closest point (GICP) algorithm, which can accurately register sparse clouds where unmodified GICP would fail. This paper builds on that work by expanding the comparison between the two algorithms across multiple data sets at a greater range of distances. The results confirm that Mesh-GICP is more accurate, more precise, and faster. They also show that it can successfully register scans 4–17 times further apart than GICP. In two different experiments this paper uses Mesh-GICP to compare three different registration methods—pairwise, metascan, keyscan—in two different situations, one in a visual odometry (VO) style, and another in a mapping style. The results of these experiments show that the keyscan method is the most accurate of the three so long as there is sufficient overlap between the target and source clouds. Where there is unsufficient overlap, pairwise matching is more accurate.en
dc.identifier.citationYoung M, Pretty C, McCulloch J, Green R Sparse point cloud registration and aggregation with mesh‐based generalized iterative closest point. Journal of Field Robotics.en
dc.identifier.doihttp://doi.org/10.1002/rob.22032
dc.identifier.issn1556-4959
dc.identifier.issn1556-4967
dc.identifier.urihttps://hdl.handle.net/10092/102808
dc.languageen
dc.language.isoen
dc.publisherWileyen
dc.rightsAll rights reserved unless otherwise stateden
dc.rights.urihttp://hdl.handle.net/10092/17651en
dc.subjectPCLen
dc.subjectsparse point clouden
dc.subjectGICPen
dc.subjectregistrationen
dc.subject.anzsrc0801 Artificial Intelligence and Image Processingen
dc.subject.anzsrc0906 Electrical and Electronic Engineeringen
dc.subject.anzsrc0913 Mechanical Engineeringen
dc.subject.anzsrcFields of Research::40 - Engineering::4007 - Control engineering, mechatronics and robotics::400702 - Automation engineeringen
dc.subject.anzsrcFields of Research::40 - Engineering::4007 - Control engineering, mechatronics and robotics::400706 - Field roboticsen
dc.subject.anzsrcFields of Research::46 - Information and computing sciences::4601 - Applied computing::460106 - Spatial data and applicationsen
dc.subject.anzsrcFields of Research::46 - Information and computing sciences::4613 - Theory of computation::461305 - Data structures and algorithmsen
dc.titleSparse point cloud registration and aggregation with mesh‐based generalized iterative closest pointen
dc.typeJournal Articleen
uc.collegeFaculty of Engineering
uc.departmentMechanical Engineering
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