Comparison of surface normal estimation methodsfor range sensing applications

Klaas Klasing, Daniel Althoff, Dirk Wollherr, Martin Buss

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

253 Scopus citations

Abstract

As mobile robotics is gradually moving towards a, level of semantic environment understanding, robust 3D object, recognition plays an increasingly important role. One of the, most crucial prerequisites for object recognition is a set of fast, algorithms for geometry segmentation and extraction, which in, turn rely on surface normal vectors as a fundamental feature., Although there exists a plethora of different approaches for, estimating normal vectors from 3D point clouds, it is largely, unclear which methods are preferable for online processing on, a mobile robot. This paper presents a detailed analysis and, comparison of existing methods for surface normal estimation, with a special emphasis on the trade-off between quality and, speed. The study sheds light on the computational complexity, as well as the qualitative differences between methods and, provides guidelines on choosing the 'right' algorithm for the, robotics practitioner. The robustness of the methods with re-, spect to noise and neighborhood size is analyzed. All algorithms, are benchmarked with simulated as well as real 3D laser data, obtained from a mobile robot.

Original languageEnglish
Title of host publication2009 IEEE International Conference on Robotics and Automation, ICRA '09
Pages3206-3211
Number of pages6
DOIs
StatePublished - 2009
Event2009 IEEE International Conference on Robotics and Automation, ICRA '09 - Kobe, Japan
Duration: 12 May 200917 May 2009

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
ISSN (Print)1050-4729

Conference

Conference2009 IEEE International Conference on Robotics and Automation, ICRA '09
Country/TerritoryJapan
CityKobe
Period12/05/0917/05/09

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