Loss of Significance and Its Effect on Point Normal Orientation and Cloud Registration (2019)
Point normal calculation and cloud registration are two of the most common operations in point cloud processing. However, both are vulnerable to issues of numerical precision and loss of significance. This paper documents how loss of significance in the open-source Point Cloud Library can create erroneous point normals and cause cloud registration to fail. Several test clouds are used to demonstrate how the loss of significance is caused by tight point spacing and clouds being shifted far from the origin of their coordinate system. The results show that extreme loss of significance can cause point normals to be calculated with a random orientation, and cause meters of error during cloud registration. Depending on the structure of the point cloud, loss of significance can occur when the cloud is at hundreds or even tens of meters from the origin of its coordinate system. Shifting to larger data types (e.g., from 32-bit “floats” to 64-bit “doubles”) can alleviate the problem but will not solve it completely. Several “best practice” recommendations for avoiding this issue are proposed. But the only solution guaranteed to eliminate loss of significance is de-meaning the entire cloud, or clusters of points before processing.
CitationYoung M, Pretty C, Agostinho S, Green R, Chen X Loss of Significance and Its Effect on Point Normal Orientation and Cloud Registration. Remote Sensing. 11(11). 1329-1329.
This citation is automatically generated and may be unreliable. Use as a guide only.
Keywordspoint normal; point clouds; numerical precision; LiDAR; Point Cloud Library; Iterative Closest Point
ANZSRC Fields of Research08 - Information and Computing Sciences::0802 - Computation Theory and Mathematics::080205 - Numerical Computation
08 - Information and Computing Sciences::0801 - Artificial Intelligence and Image Processing::080103 - Computer Graphics
08 - Information and Computing Sciences::0801 - Artificial Intelligence and Image Processing::080104 - Computer Vision
46 - Information and computing sciences::4602 - Artificial intelligence::460207 - Modelling and simulation
08 - Information and Computing Sciences::0804 - Data Format
Rights© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Showing items related by title, author, creator and subject.
Sparse point cloud registration and aggregation with mesh‐based generalized iterative closest point Young M; McCulloch J; Green R; Pretty, Christopher (Wiley, 2021)Accurate 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 ...
Holling, K. (University of Canterbury, 1983)The purpose of this report is to describe the design and implementation of a simple two dimensional drawing system for use from within the Computer Science Department's DG SIMULA implementation. This includes a description ...
Point cloud technology and 2D computational flow dynamic modeling for rapid hazards and disaster risk appraisal on Yellow Creek fan, Southern Alps of New Zealand Gomez C; Purdie HL (2018)Glacial recession in Alpine Valleys uncorks low-altitude tributary valleys that spill trapped sediments in the sediment cascade, in turn generating sediment-related hazards and flood hazards. Within this complex, the present ...