Normal distributions transform: Difference between revisions

Revision as of 14:31, 27 February 2022

Revision as of 21:22, 27 February 2022

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The algorithm registers two point clouds by first associating a [[piecewise]] [[normal distribution|normal distribution]] to the first point cloud, that gives the probability of sampling a point belonging to the cloud at a given spatial coordinate, and then finding a transform that maps the second point cloud to the first by maximising the [[likelihood function|likelihood]] of the second point cloud on such distribution as a function of the transform parameters.

Originally introduced for 2D point cloud map matching in [[simultaneous localization and mapping]] (SLAM) and relative position tracking,<ref name="biberAndStraßer">Biber and Straßer (2003)</ref> the algorithm canwas be appliedextended to 3D point clouds<ref>Magnusson (2009)</ref> and has wide applications in [[computer vision]] and [[robotics]]. NDT is very fast and accurate and it is suitable for application to large scale data, but it is also sensitive to initialisation, requiring a sufficiently accurate initial guess, and for this reason it is typically used in a coarse-to-fine alignment strategy.<ref>Dong et al. (2020)</ref><ref>Li et al. (2021)</ref><ref>Cheng et al. (2018)</ref>

== Formulation ==

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== Sources ==

*{{cite conference|title=The normal distributions transform: A new approach to laser scan matching|book-title=Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003)(Cat. No. 03CH37453)|year=2003|volume=3|last1=Biber|first1=Peter|last2=Straßer|first2=Wolfgang}}

*{{cite journal|title=Registration of laser scanning point clouds: A review|year=2018|journal=Sensors|volume=18|pages=1641|issue=5|publisher=Multidisciplinary Digital Publishing Institute|last1=Cheng|first1=Liang|last2=Chen|first2=Song|last3=Liu|first3=Xiaoqiang|last4=Xu|first4=Hao|last5=Wu|first5=Yang|last6=Li|first6=Manchun|last7=Chen|first7=Yanming}}

*{{cite journal|title=Registration of large-scale terrestrial laser scanner point clouds: A review and benchmark|year=2020|journal=ISPRS Journal of Photogrammetry and Remote Sensing|volume=163|pages=327–342|publisher=Elsevier|last1=Dong|first1=Zhen|last2=Liang|first2=Fuxun|last3=Yang|first3=Bisheng|last4=Xu|first4=Yusheng|last5=Zang|first5=Yufu|last6=Li|first6=Jianping|last7=Wang|first7=Yuan|last8=Dai|first8=Wenxia|last9=Fan|first9=Hongchao|last10=Hyyppä|first10=Juha}}

*{{cite journal|title=A Tutorial Review on Point Cloud Registrations: Principle, Classification, Comparison, and Technology Challenges|year=2021|journal=Mathematical Problems in Engineering|volume=2021|publisher=Hindawi|last1=Li|first1=Leihui|last2=Wang|first2=Riwei|last3=Zhang|first3=Xuping}}

*{{cite thesis|type=Ph.D.|title=The three-dimensional normal-distributions transform: an efficient representation for registration, surface analysis, and loop detection|year=2009|publisher=Örebro universitet|last1=Magnusson|first1=Martin}}