Point cloud registration using congruent pyramids

Aravindhan K. Krishnan; Srikanth Saripalli

doi:10.1109/IROS.2014.6942800

Point cloud registration using congruent pyramids

Aravindhan K. Krishnan, Srikanth Saripalli

CLAS-NS: Earth and Space Exploration, School of (SESE)

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Scopus citations

Abstract

We present a method to compute an initial alignment for pairwise registration of point clouds. This method uses the properties of a rigid body transformation - the ratio of lengths is preserved, the euclidean distance between points is preserved - to find congruent pyramids in two point clouds. The corresponding vertices of the congruent pyramids are used to derive a closed form solution for initial alignment. The alignment is refined further using the Iterative Closest Point algorithm. We validate the method on challenging datasets - which include airborne LIDAR, outdoor, and indoor - having initial offsets and varying densities.

Original language	English (US)
Title of host publication	IROS 2014 Conference Digest - IEEE/RSJ International Conference on Intelligent Robots and Systems
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1812-1817
Number of pages	6
ISBN (Electronic)	9781479969340
DOIs	https://doi.org/10.1109/IROS.2014.6942800
State	Published - Oct 31 2014
Event	2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2014 - Chicago, United States Duration: Sep 14 2014 → Sep 18 2014

Publication series

Name	IEEE International Conference on Intelligent Robots and Systems
ISSN (Print)	2153-0858
ISSN (Electronic)	2153-0866

Other

Other	2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2014
Country	United States
City	Chicago
Period	9/14/14 → 9/18/14

ASJC Scopus subject areas

Control and Systems Engineering
Software
Computer Vision and Pattern Recognition
Computer Science Applications

Access to Document

10.1109/IROS.2014.6942800

Cite this

Krishnan, A. K., & Saripalli, S. (2014). Point cloud registration using congruent pyramids. In IROS 2014 Conference Digest - IEEE/RSJ International Conference on Intelligent Robots and Systems (pp. 1812-1817). [6942800] (IEEE International Conference on Intelligent Robots and Systems). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IROS.2014.6942800

Point cloud registration using congruent pyramids. / Krishnan, Aravindhan K.; Saripalli, Srikanth.

IROS 2014 Conference Digest - IEEE/RSJ International Conference on Intelligent Robots and Systems. Institute of Electrical and Electronics Engineers Inc., 2014. p. 1812-1817 6942800 (IEEE International Conference on Intelligent Robots and Systems).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Krishnan, AK & Saripalli, S 2014, Point cloud registration using congruent pyramids. in IROS 2014 Conference Digest - IEEE/RSJ International Conference on Intelligent Robots and Systems., 6942800, IEEE International Conference on Intelligent Robots and Systems, Institute of Electrical and Electronics Engineers Inc., pp. 1812-1817, 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2014, Chicago, United States, 9/14/14. https://doi.org/10.1109/IROS.2014.6942800

Krishnan AK, Saripalli S. Point cloud registration using congruent pyramids. In IROS 2014 Conference Digest - IEEE/RSJ International Conference on Intelligent Robots and Systems. Institute of Electrical and Electronics Engineers Inc. 2014. p. 1812-1817. 6942800. (IEEE International Conference on Intelligent Robots and Systems). https://doi.org/10.1109/IROS.2014.6942800

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AB - We present a method to compute an initial alignment for pairwise registration of point clouds. This method uses the properties of a rigid body transformation - the ratio of lengths is preserved, the euclidean distance between points is preserved - to find congruent pyramids in two point clouds. The corresponding vertices of the congruent pyramids are used to derive a closed form solution for initial alignment. The alignment is refined further using the Iterative Closest Point algorithm. We validate the method on challenging datasets - which include airborne LIDAR, outdoor, and indoor - having initial offsets and varying densities.

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Point cloud registration using congruent pyramids

Abstract

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