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NDT-Transformer: Large-Scale 3D Point Cloud Localisation using the Normal Distribution Transform Representation
Visual Computing Group, University of Sheffield, Sheffield, UK.
Department of Engineering Science, University of Oxford, Oxford, UK.
Örebro University, School of Science and Technology. (Mobile Robotics and Olfaction lab)ORCID iD: 0000-0003-2504-2488
IMT Lab, Xiamen University, Xiamen, China.
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2021 (English)In: 2021 IEEE International Conference on Robotics and Automation (ICRA), IEEE, 2021Conference paper, Published paper (Refereed)
Abstract [en]

3D point cloud-based place recognition is highly demanded by autonomous driving in GPS-challenged environments and serves as an essential component (i.e. loop-closure detection) in lidar-based SLAM systems. This paper proposes a novel approach, named NDT-Transformer, for real-time and large-scale place recognition using 3D point clouds. Specifically, a 3D Normal Distribution Transform (NDT) representation is employed to condense the raw, dense 3D point cloud as probabilistic distributions (NDT cells) to provide the geometrical shape description. Then a novel NDT-Transformer network learns a global descriptor from a set of 3D NDT cell representations. Benefiting from the NDT representation and NDT-Transformer network, the learned global descriptors are enriched with both geometrical and contextual information. Finally, descriptor retrieval is achieved using a query-database for place recognition. Compared to the state-of-the-art methods, the proposed approach achieves an improvement of 7.52% on average top 1 recall and 2.73% on average top 1% recall on the Oxford Robotcar benchmark.

Place, publisher, year, edition, pages
IEEE, 2021.
Series
IEEE International Conference on Robotics and Automation (ICRA), ISSN 1050-4729, E-ISSN 2577-087X
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:oru:diva-96652DOI: 10.1109/ICRA48506.2021.9560932ISI: 000765738804041Scopus ID: 2-s2.0-85124680724ISBN: 9781728190778 (electronic)ISBN: 9781728190785 (print)OAI: oai:DiVA.org:oru-96652DiVA, id: diva2:1631597
Conference
2021 IEEE International Conference on Robotics and Automation (ICRA 2021), Xi'an, China, May 30 - June 5, 2021
Funder
EU, Horizon 2020, 732737
Note

Funding agencies:

UK Research & Innovation (UKRI)

Engineering & Physical Sciences Research Council (EPSRC) EP/R026092/1  

Royal Society of London European Commission RGS202432

Available from: 2022-01-24 Created: 2022-01-24 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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Adolfsson, Daniel

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Citation style
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