DATASET

Global matching of point clouds for scan registration and loop detection

Collection: RISE : Robust Indoor Localization in Complex Scenarios  

Description

We present a robust Global Matching technique focused on 3D mapping applications using laser range-finders. Our approach works under the assumption that places can be recognized by analyzing the projection of the observed points along the gravity direction. Relative poses between pairs of 3D point clouds are estimated by aligning their 2D projective representations and benefiting from the corresponding dimensional reduction. We present the complete processing pipeline for two different applications that use the global matcher as a core component: First, the global matcher is used for the registration of static scan sets where no a-priori information of the relative poses is available. It is combined with an effective procedure for validating the matches that exploits the implicit empty space information associated to single acquisitions. In the second use case, the global matcher is used for the loop detection required for 3D SLAM applications. We use an Extended Kalman Filter to obtain a belief of the map poses, which allows to validate matches and to execute hierarchical overlap tests, which reduce the number of potential matches to be evaluated. Additionally, the global matcher is combined with a fast local technique. In both use cases, the global reconstruction problem is modeled as a sparse graph, where scan poses (nodes) are connected through matches (edges). The graph structure allows formulating a sparse global optimization problem that optimizes scan poses, considering simultaneously all accepted matches. Our approach is being used in production systems and has been successfully evaluated on several real and publicly available datasets.

Contact

Email
Carlos.SANCHEZ-BELENGUER (at) ec.europa.eu

Contributors

How to cite

European Commission, Joint Research Centre (JRC) (2021): Global matching of point clouds for scan registration and loop detection. European Commission, Joint Research Centre (JRC) [Dataset] PID: http://data.europa.eu/89h/8c002004-8cd1-4998-89e1-8875a386743a

Data access

Paper datasets
URL 
  • Static scanner datasets used to evaluate the approach presented in the paper.

Publications

Publication
Global Matching of Point Clouds for Scan Registration and Loop Detection
Sanchez Belenguer, C., Ceriani, S., Taddei, P., Wolfart, E. and Sequeira, V., Global Matching of Point Clouds for Scan Registration and Loop Detection, ROBOTICS AND AUTONOMOUS SYSTEMS, ISSN 0921-8890 (online), 123, 2020, p. 103324, JRC106877.
  • ELSEVIER SCIENCE BV, AMSTERDAM, NETHERLANDS
Publication page 
  • Abstract

    We present a robust Global Matching technique focused on 3D mapping applications using laser rangefinders.Our approach works under the assumption that places can be recognized by analyzing the projection of the observed points along the gravity direction. Relative poses between pairs of 3D point clouds are estimated by aligning their 2D projective representations and benefiting from the corresponding dimensional reduction. We present the complete processing pipeline for two different applications that use the global matcher as a core component: First, the global matcher is used for the registration of static scan sets where no a-priori information of the relative poses is available. It is combined with an effective procedure for validating the matches that exploits the implicit empty space information associated to single acquisitions. In the second use case, the global matcher is used for the loop detection required for 3D SLAM applications. We use an Extended Kalman Filter to obtain a belief of the map poses, which allows to validate matches and to execute hierarchical overlap tests, which reduce the number of potential matches to be evaluated. Additionally, the global matcher is combined with a fast local technique. In both use cases, the global reconstruction problem is modeled as a sparse graph, where scan poses (nodes) are connected through matches (edges). The graph structure allows formulating a sparse global optimization problem that optimizes scan poses, considering simultaneously all accepted matches. Our approach is being used in production systems and has been successfully evaluated on several real and publicly available datasets.

Additional information

Published by
European Commission, Joint Research Centre
Created date
2021-05-19
Modified date
2021-05-25
Issued date
2021-05-19
Data theme(s)
Science and technology
Update frequency
irregular
Identifier
http://data.europa.eu/89h/8c002004-8cd1-4998-89e1-8875a386743a
Popularity
11 May 2022: 1 visits