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A decentralized sensor fusion scheme for multi sensorial fault resilient pose estimation
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.ORCID iD: 0000-0003-3530-1084
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.ORCID iD: 0000-0003-3557-6782
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.ORCID iD: 0000-0002-6415-6982
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.ORCID iD: 0000-0001-7631-002x
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2021 (English)In: Sensors, E-ISSN 1424-8220, Vol. 21, no 24, article id 8259Article in journal (Refereed) Published
Abstract [en]

This article proposes a novel decentralized two-layered and multi-sensorial based fusion architecture for establishing a novel resilient pose estimation scheme. As it will be presented, the first layer of the fusion architecture considers a set of distributed nodes. All the possible combinations of pose information, appearing from different sensors, are integrated to acquire various possibilities of estimated pose obtained by involving multiple extended Kalman filters. Based on the estimated poses, obtained from the first layer, a Fault Resilient Optimal Information Fusion (FR-OIF) paradigm is introduced in the second layer to provide a trusted pose estimation. The second layer incorporates the output of each node (constructed in the first layer) in a weighted linear combination form, while explicitly accounting for the maximum likelihood fusion criterion. Moreover, in the case of inaccurate measurements, the proposed FR-OIF formulation enables a self resiliency by embedding a built-in fault isolation mechanism. Additionally, the FR-OIF scheme is also able to address accurate localization in the presence of sensor failures or erroneous measurements. To demonstrate the effectiveness of the proposed fusion architecture, extensive experimental studies have been conducted with a micro aerial vehicle, equipped with various onboard pose sensors, such as a 3D lidar, a real-sense camera, an ultra wide band node, and an IMU. The efficiency of the proposed novel framework is extensively evaluated through multiple experimental results, while its superiority is also demonstrated through a comparison with the classical multi-sensorial centralized fusion approach. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Place, publisher, year, edition, pages
MDPI, 2021. Vol. 21, no 24, article id 8259
Keywords [en]
Information fusion, Kalman filters, Maximum likelihood estimation, Micro air vehicle (MAV), Decentralised, Decentralized fusion, Fault resilient optimal information fusion, Fusion architecture, Linear minimum variance, Maximum likelihood function, Multi-sensor fusion, Optimal information filter, Optimal information fusion, Pose-estimation, Antennas
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Robotics and Artificial Intelligence
Identifiers
URN: urn:nbn:se:ltu:diva-88527DOI: 10.3390/s21248259ISI: 000737322300001PubMedID: 34960352Scopus ID: 2-s2.0-85120770377OAI: oai:DiVA.org:ltu-88527DiVA, id: diva2:1621797
Funder
EU, Horizon 2020, 101003591 NEXGEN SIMS
Note

Validerad;2022;Nivå 2;2022-01-01 (johcin)

Available from: 2021-12-20 Created: 2021-12-20 Last updated: 2024-02-22Bibliographically approved
In thesis
1. Platform-Agnostic Resilient Decentralized Multi-Sensor Fusion for Pose Estimation
Open this publication in new window or tab >>Platform-Agnostic Resilient Decentralized Multi-Sensor Fusion for Pose Estimation
2024 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis presents an innovative decentralised sensor fusion framework with significant potential to improve navigation accuracy in autonomous vehicles. Its applicability is especially noteworthy in demanding scenarios, such as adverse weather conditions and intricate urban environments. In general, sensor fusion is a crucial method for integrating signals from various sources, extracting and integrating information from multiple inputs into a unified signal or data set. Frequently, sources of information are from sensors or devices designed for the perception and measurement of dynamic environmental changes. The collected data from diverse sensors undergoes processing through specialised algorithms, commonly referred to as "sensor fusion" or "data fusion" algorithms. This thesis describes sensor fusion's significance in processing data from multiple sources. It highlights the classification of fusion algorithms, demonstrating the versatility and applicability of sensor fusion across a range of redundant sensors. Moreover, various creative strategies for sensor fusion, including fault detection and isolation and methods for addressing non-Gaussian noise through smoothing filter techniques, are collectively introduced as part of a comprehensive navigation framework. The contributions of this thesis are summarized in the following. First, it introduces a decentralised two-layered fusion architecture for pose estimation, emphasising fault resilience. In a decentralised fashion, it utilises distributed nodes equipped with extended Kalman filters in the initial tier and optimal information filters in the subsequent tier to amalgamate pose data from multiple sensors. The design is named the Fault-Resilient Optimal Information Fusion (FR-OIF) architecture in this thesis, which guarantees reliable pose estimation, even in cases of sensor malfunctions. Secondly, this work proposes an Auto-encoder-based fault detection framework for a multi-sensorial distributed pose estimation. In this framework, auto-encoders are applied to detect anomalies in the raw signal measurements. At the same time, a fault-resilient optimal information filter (FROIF) approach is incorporated with the auto-encoder-based detection to improve estimation accuracy. The effectiveness of these methods is demonstrated through experimental results involving a micro aerial vehicle and is compared to a novel classical detection approach based on the Extended Kalman filter. Furthermore, it introduces an integrated multi-sensor fusion architecture enhanced by centralised Auto-encoder technology and an EKF framework. This approach effectively removes sensor data noise and anomalies, ensuring reliable data reconstruction, even when faced with time-dependent anomalies. The assessment of the framework's performance using actual sensor data collected from the onboard sensors of a micro aerial vehicle demonstrates its superiority compared to a centralised Extended Kalman filter without Auto-encoders. The next part of the thesis discusses the increasing need for resilient autonomy in complex space missions. It emphasises the challenges posed by interactions with non-cooperative objects and extreme environments, calling for advanced autonomy solutions.  Furthermore, this work introduces a decentralised multi-sensor fusion architecture for resilient satellite navigation around asteroids. It addresses challenges such as dynamic illumination, sensor drift, and momentary sensor failure. The approach includes fault detection and isolation methods, ensuring autonomous operation in adverse conditions. Finally, the last part of the thesis focuses on accurate localisation and deviation identification in multi-sensor fusion with Millimeter-Wave Radars. It presents a flexible, decentralised smoothing filter framework that effectively handles unwanted measurements and enhances Ego velocity estimation accuracy.  Overall, this thesis plays a significant role in advancing the field of decentralised sensor fusion, encompassing anomaly avoidance mechanisms, fault detection and isolation frameworks, and robust navigation algorithms applicable across a range of domains, covering everything from robotics to space exploration. In the initial section of this thesis, we delve into the backdrop, reasons behind the research, existing challenges, and the contributions made. Conversely, the subsequent section comprises the complete articles linked to the outlined contributions and a bibliography.

Place, publisher, year, edition, pages
Luleå: Luleå University of Technology, 2024. p. 184
Series
Licentiate thesis / Luleå University of Technology, ISSN 1402-1757
National Category
Signal Processing
Research subject
Robotics and Artificial Intelligence
Identifiers
urn:nbn:se:ltu:diva-104362 (URN)978-91-8048-488-6 (ISBN)978-91-8048-489-3 (ISBN)
Presentation
2024-04-03, A1545, Luleå University of Technology, Luleå, 13:00 (English)
Opponent
Supervisors
Available from: 2024-02-22 Created: 2024-02-22 Last updated: 2024-03-13Bibliographically approved

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Mukherjee, MoumitaBanerjee, AvijitPapadimitriou, AndreasMansouri, Sina SharifNikolakopoulos, George

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