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BFAR – Bounded False Alarm Rate detector for improved radar odometry estimation
School of Science and Technology, Örebro University, Örebro, Sweden; Computer Engineering Department, University of Baghdad, Baghdad, Iraq. (Center for Applied Autonomous Sensor Systems (AASS))
Örebro University, School of Science and Technology. (Center for Applied Autonomous Sensor Systems (AASS))ORCID iD: 0000-0003-2504-2488
Örebro University, School of Science and Technology. (Center for Applied Autonomous Sensor Systems (AASS))ORCID iD: 0000-0001-8658-2985
Örebro University, School of Science and Technology. (Center for Applied Autonomous Sensor Systems (AASS))ORCID iD: 0000-0002-2953-1564
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2021 (English)Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents a new detector for filtering noise from true detections in radar data, which improves the state of the art in radar odometry. Scanning Frequency-Modulated Continuous Wave (FMCW) radars can be useful for localisation and mapping in low visibility, but return a lot of noise compared to (more commonly used) lidar, which makes the detection task more challenging. Our Bounded False-Alarm Rate (BFAR) detector is different from the classical Constant False-Alarm Rate (CFAR) detector in that it applies an affine transformation on the estimated noise level after which the parameters that minimize the estimation error can be learned. BFAR is an optimized combination between CFAR and fixed-level thresholding. Only a single parameter needs to be learned from a training dataset. We apply BFAR tothe use case of radar odometry, and adapt a state-of-the-art odometry pipeline (CFEAR), replacing its original conservative filtering with BFAR. In this way we reduce the state-of-the-art translation/rotation odometry errors from 1.76%/0.5◦/100 m to 1.55%/0.46◦/100 m; an improvement of 12.5%.

Place, publisher, year, edition, pages
2021.
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:oru:diva-108800OAI: oai:DiVA.org:oru-108800DiVA, id: diva2:1803369
Conference
ICRA
Funder
Knowledge FoundationAvailable from: 2023-10-09 Created: 2023-10-09 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, HenrikLilienthal, Achim

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