RANSAC matching: Simultaneous registration and segmentation

Shao Wen Yang*, Chieh-Chih Wang, Chun Hua Chang

*Corresponding author for this work

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

30 Scopus citations


The iterative closest points (ICP) algorithm is widely used for ego-motion estimation in robotics, but subject to bias in the presence of outliers. We propose a random sample consensus (RANSAC) based algorithm to simultaneously achieving robust and realtime ego-motion estimation, and multiscale segmentation in environments with rapid changes. Instead of directly sampling on measurements, RANSAC matching investigates initial estimates at the object level of abstraction for systematic sampling and computational efficiency. A soft segmentation method using a multi-scale representation is exploited to eliminate segmentation errors. By explicitly taking into account the various noise sources degrading the effectiveness of geometric alignment: sensor noise, dynamic objects and data association uncertainty, the uncertainty of a relative pose estimate is calculated under a theoretical investigation of scoring in the RANSAC paradigm. The improved segmentation can also be used as the basis for higher level scene understanding. The effectiveness of our approach is demonstrated qualitatively and quantitatively through extensive experiments using real data.

Original languageEnglish
Title of host publication2010 IEEE International Conference on Robotics and Automation, ICRA 2010
Number of pages8
StatePublished - 26 Aug 2010
Event2010 IEEE International Conference on Robotics and Automation, ICRA 2010 - Anchorage, AK, United States
Duration: 3 May 20107 May 2010

Publication series

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


Conference2010 IEEE International Conference on Robotics and Automation, ICRA 2010
CountryUnited States
CityAnchorage, AK

Fingerprint Dive into the research topics of 'RANSAC matching: Simultaneous registration and segmentation'. Together they form a unique fingerprint.

Cite this