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Scan registration for autonomous mining vehicles using 3D‐NDT
Author(s) -
Magnusson Martin,
Lilienthal Achim,
Duckett Tom
Publication year - 2007
Publication title -
journal of field robotics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.152
H-Index - 96
eISSN - 1556-4967
pISSN - 1556-4959
DOI - 10.1002/rob.20204
Subject(s) - iterative closest point , computer vision , artificial intelligence , computer science , representation (politics) , generalization , point (geometry) , robot , range (aeronautics) , point cloud , image registration , laser scanning , algorithm , image (mathematics) , engineering , mathematics , laser , mathematical analysis , physics , geometry , optics , politics , law , political science , aerospace engineering
Scan registration is an essential subtask when building maps based on range finder data from mobile robots. The problem is to deduce how the robot has moved between consecutive scans, based on the shape of overlapping portions of the scans. This paper presents a new algorithm for registration of 3D data. The algorithm is a generalization and improvement of the normal distributions transform (NDT) for 2D data developed by Biber and Strasser, which allows for accurate registration using a memory‐efficient representation of the scan surface. A detailed quantitative and qualitative comparison of the new algorithm with the 3D version of the popular ICP (iterative closest point) algorithm is presented. Results with actual mine data, some of which were collected with a new prototype 3D laser scanner, show that the presented algorithm is faster and slightly more reliable than the standard ICP algorithm for 3D registration, while using a more memory‐efficient scan surface representation. © 2007 Wiley Periodicals, Inc.