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Almqvist, H., Magnusson, M., Kucner, T. P. & Lilienthal, A. (2018). Learning to detect misaligned point clouds. Journal of Field Robotics, 35(5), 662-677
Open this publication in new window or tab >>Learning to detect misaligned point clouds
2018 (English)In: Journal of Field Robotics, ISSN 1556-4959, E-ISSN 1556-4967, Vol. 35, no 5, p. 662-677Article in journal (Refereed) Published
Abstract [en]

Matching and merging overlapping point clouds is a common procedure in many applications, including mobile robotics, three-dimensional mapping, and object visualization. However, fully automatic point-cloud matching, without manual verification, is still not possible because no matching algorithms exist today that can provide any certain methods for detecting misaligned point clouds. In this article, we make a comparative evaluation of geometric consistency methods for classifying aligned and nonaligned point-cloud pairs. We also propose a method that combines the results of the evaluated methods to further improve the classification of the point clouds. We compare a range of methods on two data sets from different environments related to mobile robotics and mapping. The results show that methods based on a Normal Distributions Transform representation of the point clouds perform best under the circumstances presented herein.

Place, publisher, year, edition, pages
John Wiley & Sons, 2018
Keywords
perception, mapping, position estimation
National Category
Computer Sciences Computer Vision and Robotics (Autonomous Systems)
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-62985 (URN)10.1002/rob.21768 (DOI)000437836900002 ()2-s2.0-85037622789 (Scopus ID)
Projects
ILIADALLO
Funder
EU, Horizon 2020, 732737Knowledge Foundation, 20110214
Available from: 2017-12-05 Created: 2017-12-05 Last updated: 2018-07-27Bibliographically approved
Almqvist, H., Magnusson, M. & Lilienthal, A. J. (2014). Improving Point Cloud Accuracy Obtained from a Moving Platform for Consistent Pile Attack Pose Estimation. Journal of Intelligent and Robotic Systems, 75(1), 101-128
Open this publication in new window or tab >>Improving Point Cloud Accuracy Obtained from a Moving Platform for Consistent Pile Attack Pose Estimation
2014 (English)In: Journal of Intelligent and Robotic Systems, ISSN 0921-0296, E-ISSN 1573-0409, Vol. 75, no 1, p. 101-128Article in journal (Refereed) Published
Abstract [en]

We present a perception system for enabling automated loading with waist-articulated wheel loaders. To enable autonomous loading of piled materials, using either above-ground wheel loaders or underground load-haul-dump vehicles, 3D data of the pile shape is needed. However, using common 3D scanners, the scan data is distorted while the wheel loader is moving towards the pile. Existing methods that make use of 3D scan data (for autonomous loading as well as tasks such as mapping, localisation, and object detection) typically assume that each 3D scan is accurate. For autonomous robots moving over rough terrain, it is often the case that the vehicle moves a substantial amount during the acquisition of one 3D scan, in which case the scan data will be distorted. We present a study of auto-loading methods, and how to locate piles in real-world scenarios with nontrivial ground geometry. We have compared how consistently each method performs for live scans acquired in motion, and also how the methods perform with different view points and scan configurations. The system described in this paper uses a novel method for improving the quality of distorted 3D scans made from a vehicle moving over uneven terrain. The proposed method for improving scan quality is capable of increasing the accuracy of point clouds without assuming any specific features of the environment (such as planar walls), without resorting to a “stop-scan-go” approach, and without relying on specialised and expensive hardware. Each new 3D scan is registered to the preceding using the normal-distributions transform (NDT). After each registration, a mini-loop closure is performed with a local, per-scan, graph-based SLAM method. To verify the impact of the quality improvement, we present data that shows how auto-loading methods benefit from the corrected scans. The presented methods are validated on data from an autonomous wheel loader, as well as with simulated data. The proposed scan-correction method increases the accuracy of both the vehicle trajectory and the point cloud. We also show that it increases the reliability of pile-shape measures used to plan an efficient attack pose when performing autonomous loading.

Place, publisher, year, edition, pages
Dordrecht: Springer Netherlands, 2014
Keywords
3D perception, Autoloading, Scanning while moving
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-34209 (URN)10.1007/s10846-013-9957-9 (DOI)000337053500009 ()2-s2.0-84902547213 (Scopus ID)
Projects
Allo
Funder
Knowledge Foundation, 20110214
Available from: 2014-03-13 Created: 2014-03-12 Last updated: 2018-01-11Bibliographically approved
Almqvist, H., Magnusson, M., Stoyanov, T. & Lilienthal, A. J. (2013). Improving Point-Cloud Accuracy from a Moving Platform in Field Operations. In: 2013 IEEE International Conference on Robotics and Automation (ICRA): . Paper presented at 2013 IEEE International Conference on Robotics and Automation (ICRA, 6-10 May, Karlsruhe (pp. 733-738). IEEE conference proceedings
Open this publication in new window or tab >>Improving Point-Cloud Accuracy from a Moving Platform in Field Operations
2013 (English)In: 2013 IEEE International Conference on Robotics and Automation (ICRA), IEEE conference proceedings, 2013, p. 733-738Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents a method for improving the quality of distorted 3D point clouds made from a vehicle equipped with a laser scanner moving over uneven terrain. Existing methods that use 3D point-cloud data (for tasks such as mapping, localisation, and object detection) typically assume that each point cloud is accurate. For autonomous robots moving in rough terrain, it is often the case that the vehicle moves a substantial amount during the acquisition of one point cloud, in which case the data will be distorted. The method proposed in this paper is capable of increasing the accuracy of 3D point clouds, without assuming any specific features of the environment (such as planar walls), without resorting to a "stop-scan-go" approach, and without relying on specialised and expensive hardware. Each new point cloud is matched to the previous using normal-distribution-transform (NDT) registration, after which a mini-loop closure is performed with a local, per-scan, graph-based SLAM method. The proposed method increases the accuracy of both the measured platform trajectory and the point cloud. The method is validated on both real-world and simulated data.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2013
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-30529 (URN)10.1109/ICRA.2013.6630654 (DOI)000337617300107 ()2-s2.0-84887272249 (Scopus ID)978-1-4673-5641-1 (ISBN)978-1-4673-5643-5 (ISBN)
Conference
2013 IEEE International Conference on Robotics and Automation (ICRA, 6-10 May, Karlsruhe
Available from: 2013-08-30 Created: 2013-08-30 Last updated: 2018-01-11Bibliographically approved
Magnusson, M. & Almqvist, H. (2011). Consistent pile-shape quantification for autonomous wheel loaders. In: 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems. Paper presented at IEEE/RSJ International Conference on Intelligent Robots and Systems SEP 25-30, 2011 San Francisco, CA (pp. 4078-4083).
Open this publication in new window or tab >>Consistent pile-shape quantification for autonomous wheel loaders
2011 (English)In: 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2011, p. 4078-4083Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents a study of approaches for selecting an efficient attack pose when loading piled materials with industrial construction vehicles. Automated handling of piled materials is a highly desired goal in many construction and mining applications. The main contributions of the paper are an experimental study of two novel approaches for selecting an attack pose from 3D data, compared to previously published approaches and extensions thereof. The outcome is based on quantitative validation, both with simulated data and data from a real-world scenario with nontrivial ground geometry.

Series
IEEE International Conference on Intelligent Robots and Systems, ISSN 2153-0858
National Category
Computer Sciences
Research subject
Computer and Systems Science
Identifiers
urn:nbn:se:oru:diva-22334 (URN)000297477504065 ()978-1-61284-455-8 (ISBN)
Conference
IEEE/RSJ International Conference on Intelligent Robots and Systems SEP 25-30, 2011 San Francisco, CA
Available from: 2012-04-02 Created: 2012-04-02 Last updated: 2018-01-12Bibliographically approved
Stoyanov, T., Magnusson, M., Almqvist, H. & Lilienthal, A. J. (2011). On the Accuracy of the 3D Normal Distributions Transform as a Tool for Spatial Representation. In: 2011 IEEE International Conference on Robotics and Automation (ICRA): . Paper presented at IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA),Shanghai, PEOPLES R CHINA,MAY 09-13, 2011.. IEEE conference proceedings
Open this publication in new window or tab >>On the Accuracy of the 3D Normal Distributions Transform as a Tool for Spatial Representation
2011 (English)In: 2011 IEEE International Conference on Robotics and Automation (ICRA), IEEE conference proceedings, 2011Conference paper, Published paper (Refereed)
Abstract [en]

The Three-Dimensional Normal Distributions Transform (3D-NDT) is a spatial modeling technique with applications in point set registration, scan similarity comparison, change detection and path planning. This work concentrates on evaluating three common variations of the 3D-NDT in terms of accuracy of representing sampled semi-structured environments. In a novel approach to spatial representation quality measurement, the 3D geometrical modeling task is formulated as a classification problem and its accuracy is evaluated with standard machine learning performance metrics. In this manner the accuracy of the 3D-NDT variations is shown to be comparable to, and in some cases to outperform that of the standard occupancy grid mapping model.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2011
Series
IEEE International Conference on Robotics and Automation ICRA, ISSN 1050-4729
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Information technology
Identifiers
urn:nbn:se:oru:diva-22687 (URN)10.1109/ICRA.2011.5979584 (DOI)000324383403050 ()2-s2.0-84871695120 (Scopus ID)978-1-61284-385-8 (ISBN)
Conference
IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA),Shanghai, PEOPLES R CHINA,MAY 09-13, 2011.
Note

Proceedings at

http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5501116

Available from: 2012-05-07 Created: 2012-04-27 Last updated: 2018-01-12Bibliographically approved
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ORCID iD: ORCID iD iconorcid.org/0000-0001-5007-548X

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