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CorAl – Are the point clouds Correctly Aligned?
Örebro University, School of Science and Technology. (AASS MRO Lab)ORCID iD: 0000-0003-2504-2488
Örebro University, School of Science and Technology. (AASS MRO Lab)ORCID iD: 0000-0001-8658-2985
Örebro University, School of Science and Technology. (AASS MRO Lab)ORCID iD: 0000-0001-8119-0843
Örebro University, School of Science and Technology. (AASS MRO Lab)ORCID iD: 0000-0003-0217-9326
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2021 (English)In: 10th European Conference on Mobile Robots (ECMR 2021), IEEE, 2021, Vol. 10Conference paper, Published paper (Refereed)
Abstract [en]

In robotics perception, numerous tasks rely on point cloud registration. However, currently there is no method that can automatically detect misaligned point clouds reliably and without environment-specific parameters. We propose "CorAl", an alignment quality measure and alignment classifier for point cloud pairs, which facilitates the ability to introspectively assess the performance of registration. CorAl compares the joint and the separate entropy of the two point clouds. The separate entropy provides a measure of the entropy that can be expected to be inherent to the environment. The joint entropy should therefore not be substantially higher if the point clouds are properly aligned. Computing the expected entropy makes the method sensitive also to small alignment errors, which are particularly hard to detect, and applicable in a range of different environments. We found that CorAl is able to detect small alignment errors in previously unseen environments with an accuracy of 95% and achieve a substantial improvement to previous methods.

Place, publisher, year, edition, pages
IEEE, 2021. Vol. 10
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:oru:diva-94464DOI: 10.1109/ECMR50962.2021.9568846ISI: 000810510000059OAI: oai:DiVA.org:oru-94464DiVA, id: diva2:1596301
Conference
10th European Conference on Mobile Robots (ECMR 2021), Bonn, Germany, (Online Conference), August 31 - September 3, 2021
Funder
Knowledge FoundationEU, Horizon 2020, 732737 101017274Available from: 2021-09-22 Created: 2021-09-22 Last updated: 2024-01-02Bibliographically approved
In thesis
1. Robust large-scale mapping and localization: Combining robust sensing and introspection
Open this publication in new window or tab >>Robust large-scale mapping and localization: Combining robust sensing and introspection
2023 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The presence of autonomous systems is rapidly increasing in society and industry. To achieve successful, efficient, and safe deployment of autonomous systems, they must be navigated by means of highly robust localization systems. Additionally, these systems need to localize accurately and efficiently in realtime under adverse environmental conditions, and within considerably diverse and new previously unseen environments.

This thesis focuses on investigating methods to achieve robust large-scale localization and mapping, incorporating robustness at multiple stages. Specifically, the research explores methods with sensory robustness, utilizing radar, which exhibits tolerance to harsh weather, dust, and variations in lighting conditions. Furthermore, the thesis presents methods with algorithmic robustness, which prevent failures by incorporating introspective awareness of localization quality. This thesis aims to answer the following research questions:

How can radar data be efficiently filtered and represented for robust radar odometry? How can accurate and robust odometry be achieved with radar? How can localization quality be assessed and leveraged for robust detection of localization failures? How can self-awareness of localization quality be utilized to enhance the robustness of a localization system?

While addressing these research questions, this thesis makes the following contributions to large-scale localization and mapping: A method for robust and efficient radar processing and state-of-the-art odometry estimation, and a method for self-assessment of localization quality and failure detection in lidar and radar localization. Self-assessment of localization quality is integrated into robust systems for large-scale Simultaneous Localization And Mapping, and rapid global localization in prior maps. These systems leverage self-assessment of localization quality to improve performance and prevent failures in loop closure and global localization, and consequently achieve safe robot localization.

The methods presented in this thesis were evaluated through comparative assessments of public benchmarks and real-world data collected from various industrial scenarios. These evaluations serve to validate the effectiveness and reliability of the proposed approaches. As a result, this research represents a significant advancement toward achieving highly robust localization capabilities with broad applicability.

Place, publisher, year, edition, pages
Örebro: Örebro University, 2023. p. 72
Series
Örebro Studies in Technology, ISSN 1650-8580 ; 100
Keywords
SLAM, Localization, Robustness, Radar
National Category
Computer Sciences
Identifiers
urn:nbn:se:oru:diva-107548 (URN)9789175295244 (ISBN)
Public defence
2023-10-31, Örebro universitet, Långhuset, Hörsal L2, Fakultetsgatan 1, Örebro, 13:00 (English)
Opponent
Supervisors
Available from: 2023-08-15 Created: 2023-08-15 Last updated: 2024-01-19Bibliographically approved

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CorAl – Are the point clouds Correctly Aligned?(4797 kB)455 downloads
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Adolfsson, DanielMagnusson, MartinLiao, QianfangLilienthal, AchimAndreasson, Henrik

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