Next Article in Journal
Time-Frequency Feature Representation Using Multi-Resolution Texture Analysis and Acoustic Activity Detector for Real-Life Speech Emotion Recognition
Previous Article in Journal
Kinect as a Tool for Gait Analysis: Validation of a Real-Time Joint Extraction Algorithm Working in Side View
Article Menu

Export Article

Open AccessArticle
Sensors 2015, 15(1), 1435-1457; doi:10.3390/s150101435

A Robust Linear Feature-Based Procedure for Automated Registration of Point Clouds

MINES ParisTech, PSL–Research University, CAOR–Centre for robotics, 60 bd St Michel, 75006 Paris, France
*
Author to whom correspondence should be addressed.
Received: 24 October 2014 / Accepted: 28 November 2014 / Published: 14 January 2015
(This article belongs to the Section Remote Sensors)
View Full-Text   |   Download PDF [2471 KB, uploaded 14 January 2015]   |  

Abstract

With the variety of measurement techniques available on the market today, fusing multi-source complementary information into one dataset is a matter of great interest. Target-based, point-based and feature-based methods are some of the approaches used to place data in a common reference frame by estimating its corresponding transformation parameters. This paper proposes a new linear feature-based method to perform accurate registration of point clouds, either in 2D or 3D. A two-step fast algorithm called Robust Line Matching and Registration (RLMR), which combines coarse and fine registration, was developed. The initial estimate is found from a triplet of conjugate line pairs, selected by a RANSAC algorithm. Then, this transformation is refined using an iterative optimization algorithm. Conjugates of linear features are identified with respect to a similarity metric representing a line-to-line distance. The efficiency and robustness to noise of the proposed method are evaluated and discussed. The algorithm is valid and ensures valuable results when pre-aligned point clouds with the same scale are used. The studies show that the matching accuracy is at least 99.5%. The transformation parameters are also estimated correctly. The error in rotation is better than 2.8% full scale, while the translation error is less than 12.7%. View Full-Text
Keywords: matching; alignment; transformation; registration; point cloud; feature; line; quality; distance matching; alignment; transformation; registration; point cloud; feature; line; quality; distance
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Poreba, M.; Goulette, F. A Robust Linear Feature-Based Procedure for Automated Registration of Point Clouds. Sensors 2015, 15, 1435-1457.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top