A 3D scan matching method based on multi-layered Normal Distribution Transform

Cihan Ulas*, Hakan Temeltas

*Corresponding author for this work

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

21 Citations (Scopus)

Abstract

Scan matching plays a significant role for 3D simultaneously localization and mapping (SLAM). Before applying the SLAM methods, two 3D data which belong to highly correlated scene has to be registered by finding the correct transformation. In this paper, we introduce a multi-layered (ML) extension of 3D Normal Distribution Transform based scan matching. In this method, point cloud is subdivided into 8n equally sized cells, where n stands for the level of layer. Unlike the NDT, the score function is described as the Mahalanobis distance. In addition, Newton and Levenberg-Marquardt methods are used to optimize the score function. The proposed method is compared with original NDT, and the optimization methods are discussed. Finally, the performance evaluation is given for experimentally obtained datasets. The approximation provides much faster and long distance measurement capabilities than ordinary NDT.

Original languageEnglish
Title of host publicationProceedings of the 18th IFAC World Congress
PublisherIFAC Secretariat
Pages11602-11607
Number of pages6
Edition1 PART 1
ISBN (Print)9783902661937
DOIs
Publication statusPublished - 2011

Publication series

NameIFAC Proceedings Volumes (IFAC-PapersOnline)
Number1 PART 1
Volume44
ISSN (Print)1474-6670

Funding

This study is supported by The Scientific and Technological Research Council of Turkey under grant number 110E194.

FundersFunder number
Türkiye Bilimsel ve Teknolojik Araştirma Kurumu110E194

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