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Automatic appearance-based loop detection from three-dimensional laser data using the normal distributions transform
Örebro University, School of Science and Technology. (AASS Learning Systems Lab)ORCID iD: 0000-0001-8658-2985
Örebro University, School of Science and Technology. (AASS Learning Systems Lab)ORCID iD: 0000-0002-2953-1564
Jacobs University Bremen.
Örebro University, School of Science and Technology. (AASS Learning Systems Lab)ORCID iD: 0000-0003-0217-9326
2009 (English)In: Journal of Field Robotics, ISSN 1556-4959, E-ISSN 1556-4967, Vol. 26, no 11-12, p. 892-914Article in journal (Refereed) Published
Abstract [en]

We propose a new approach to appearance-based loop detection for mobile robots, usingthree-dimensional (3D) laser scans. Loop detection is an important problem in the simultaneouslocalization and mapping (SLAM) domain, and, because it can be seen as theproblem of recognizing previously visited places, it is an example of the data associationproblem. Without a flat-floor assumption, two-dimensional laser-based approaches arebound to fail in many cases. Two of the problems with 3D approaches that we address inthis paper are how to handle the greatly increased amount of data and how to efficientlyobtain invariance to 3D rotations.We present a compact representation of 3D point cloudsthat is still discriminative enough to detect loop closures without false positives (i.e.,detecting loop closure where there is none). A low false-positive rate is very important becausewrong data association could have disastrous consequences in a SLAM algorithm.Our approach uses only the appearance of 3D point clouds to detect loops and requires nopose information. We exploit the normal distributions transform surface representationto create feature histograms based on surface orientation and smoothness. The surfaceshape histograms compress the input data by two to three orders of magnitude. Becauseof the high compression rate, the histograms can be matched efficiently to compare theappearance of two scans. Rotation invariance is achieved by aligning scans with respectto dominant surface orientations. We also propose to use expectation maximization to fit a gamma mixture model to the output similarity measures in order to automatically determinethe threshold that separates scans at loop closures from nonoverlapping ones.Wediscuss the problem of determining ground truth in the context of loop detection and thedifficulties in comparing the results of the few available methods based on range information.Furthermore, we present quantitative performance evaluations using three realworlddata sets, one of which is highly self-similar, showing that the proposed methodachieves high recall rates (percentage of correctly identified loop closures) at low falsepositiverates in environments with different characteristics.

Place, publisher, year, edition, pages
Wiley-Blackwell, 2009. Vol. 26, no 11-12, p. 892-914
National Category
Engineering and Technology Other Computer and Information Science
Research subject
Computer and Systems Science
Identifiers
URN: urn:nbn:se:oru:diva-8431DOI: 10.1002/rob.20314ISI: 000270869800003Scopus ID: 2-s2.0-70449379456OAI: oai:DiVA.org:oru-8431DiVA, id: diva2:274842
Available from: 2009-11-08 Created: 2009-11-02 Last updated: 2018-01-12Bibliographically approved

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Magnusson, MartinAndreasson, HenrikLilienthal, Achim J.

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CiteExportLink to record
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