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Localising Faster: Efficient and precise lidar-based robot localisation in large-scale environments
Lincoln Centre for Autonomous Systems (L-CAS), University of Lincoln, UK.
Örebro University, School of Science and Technology.ORCID iD: 0000-0003-2504-2488
Örebro University, School of Science and Technology.ORCID iD: 0000-0001-8658-2985
Örebro University, School of Science and Technology.ORCID iD: 0000-0002-2953-1564
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2020 (English)In: 2020 IEEE International Conference on Robotics and Automation (ICRA), IEEE, 2020, p. 4386-4392Conference paper, Published paper (Refereed)
Abstract [en]

This paper proposes a novel approach for global localisation of mobile robots in large-scale environments. Our method leverages learning-based localisation and filtering-based localisation, to localise the robot efficiently and precisely through seeding Monte Carlo Localisation (MCL) with a deeplearned distribution. In particular, a fast localisation system rapidly estimates the 6-DOF pose through a deep-probabilistic model (Gaussian Process Regression with a deep kernel), then a precise recursive estimator refines the estimated robot pose according to the geometric alignment. More importantly, the Gaussian method (i.e. deep probabilistic localisation) and nonGaussian method (i.e. MCL) can be integrated naturally via importance sampling. Consequently, the two systems can be integrated seamlessly and mutually benefit from each other. To verify the proposed framework, we provide a case study in large-scale localisation with a 3D lidar sensor. Our experiments on the Michigan NCLT long-term dataset show that the proposed method is able to localise the robot in 1.94 s on average (median of 0.8 s) with precision 0.75 m in a largescale environment of approximately 0.5 km 2.

Place, publisher, year, edition, pages
IEEE, 2020. p. 4386-4392
Series
IEEE International Conference on Robotics and Automation (ICRA), ISSN 1050-4729, E-ISSN 2577-087X
Keywords [en]
Gaussian processes, learning (artificial intelligence), mobile robots, Monte Carlo methods, neural nets, optical radar, path planning, recursive estimation, robot vision, SLAM (robots), precise lidar-based robot localisation, large-scale environments, global localisation, Monte Carlo Localisation, MCL, fast localisation system, deep-probabilistic model, Gaussian process regression, deep kernel, precise recursive estimator, Gaussian method, deep probabilistic localisation, large-scale localisation, largescale environment, time 0.8 s, size 0.75 m, Robots, Neural networks, Three-dimensional displays, Laser radar, Kernel
National Category
Robotics
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:oru:diva-88030DOI: 10.1109/ICRA40945.2020.9196708ISI: 000712319503010Scopus ID: 2-s2.0-85092712554ISBN: 978-1-7281-7396-2 (print)ISBN: 978-1-7281-7395-5 (print)OAI: oai:DiVA.org:oru-88030DiVA, id: diva2:1524100
Conference
2020 IEEE International Conference on Robotics and Automation (ICRA), Paris, France, May 31 - August 31, 2020
Funder
EU, Horizon 2020, 732737
Note

Funding agency:

UK Research & Innovation (UKRI)

Engineering & Physical Sciences Research Council (EPSRC) EP/M019918/1

Available from: 2021-01-31 Created: 2021-01-31 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, DanielMagnusson, MartinAndreasson, Henrik

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