Normal distributions transform: Difference between revisions

Revision as of 13:19, 11 September 2022

Latest revision as of 14:36, 22 March 2023

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The '''normal distributions transform''' ('''NDT''') is a [[point-set registration|point cloud registration]] [[algorithm]] introduced by Peter Biber and Wolfgang Straßer in 2003, while working at [[University of Tübingen]].

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">{{harv|Biber|Straßer|2003}}</ref> the algorithm was extended to 3D point clouds<ref>{{harv|Magnusson|2009}}</ref> and has wide applications in [[computer vision]] and [[robotics]]. NDT is very fast and accurate, making it 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>{{harv|Dong et al.|2020}}</ref><ref>{{harv|Li|Wang|Zhang|2021|pp=21-22}}</ref><ref>{{harv|Cheng et al.|2018|pp=10-11,13}}</ref>