CorAl - Are the point clouds correctly aligned?

Daniel Adolfsson, Martin Magnusson, Qianfang Liao, Achim J. Lilienthal, Henrik Andreasson

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

5 Scopus citations


In robotics perception, numerous tasks rely on point cloud registration. However, currently there is no method that can automatically detect misaligned point clouds reliably and without environment-specific parameters. We propose "CorAl", an alignment quality measure and alignment classifier for point cloud pairs, which facilitates the ability to introspectively assess the performance of registration. CorAl compares the joint and the separate entropy of the two point clouds. The separate entropy provides a measure of the entropy that can be expected to be inherent to the environment. The joint entropy should therefore not be substantially higher if the point clouds are properly aligned. Computing the expected entropy makes the method sensitive also to small alignment errors, which are particularly hard to detect, and applicable in a range of different environments. We found that CorAl is able to detect small alignment errors in previously unseen environments with an accuracy of 95% and achieve a substantial improvement to previous methods.

Original languageEnglish
Title of host publication2021 10th European Conference on Mobile Robots, ECMR 2021 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665412131
StatePublished - Aug 2021
Externally publishedYes
Event10th European Conference on Mobile Robots, ECMR 2021 - Virtual, Bonn, Germany
Duration: 31 Aug 20213 Sep 2021

Publication series

Name2021 10th European Conference on Mobile Robots, ECMR 2021 - Proceedings


Conference10th European Conference on Mobile Robots, ECMR 2021
CityVirtual, Bonn


Dive into the research topics of 'CorAl - Are the point clouds correctly aligned?'. Together they form a unique fingerprint.

Cite this