Normal distributions transform: Difference between revisions

Revision as of 21:22, 27 February 2022

Revision as of 21:34, 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 was extended to 3D point clouds<ref>Magnusson (2009)</ref> and has wide applications in [[computer vision]] and [[robotics]]. NDT is very fast and accurate, andmaking 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 ==