Self-supervised learning at the sensor layer in robotics
department or school: Applied Science, Faculty of
department or school: Electrical and Computer Engineering, Department of
data provider: DSpace
degree campus: UBCV
creator: Yuan, Kaiwen
issued: 2023-03-01T18:23:00Z
issued: 2023
related degree: Doctor of Philosophy - PhD
degree grantor: University of British Columbia
description: Modern robots are generally armed with diverse modalities of sensors, for various functionalities and safety redundancy. The recent breakthrough of deep learning (DL) technologies demonstrates superior performance on many high-level tasks, especially with using multi-sensor fusion. While the majority of multi-modal DL methods assume that sensors are well calibrated, synchronized and denoised, such efforts at the sensor layer are non-trivial and get increasingly expensive with the growing complexity of robotic systems. Currently dominant approaches heavily rely on specific hardware setup or high-end sensors, which generally are not cost-effective. This cost concern could be a bottleneck for the potential wide adoption of low-cost robots in the near future. Even though DL has a huge potential at the sensor layer, the difficulty of acquiring sufficient and accurate annotations for related tasks remain a major challenge. This thesis first formulates key problems at the robot sensor layer from the machine learning perspective, and further proposes efficient self-supervised learning approaches systematically. In our work, the popular and representative LiDAR-camera-inertial system is utilized as the study target. Firstly, the challenging LiDAR-camera online extrinsic calibration task is delved into, and we investigate the Riemannian metrics equipped self-supervised learning approach via synthetic data. This was the first work in the literature which demonstrates competing performance of data-driven methods when compared with conventional approaches at the sensor layer. It lays the foundation and shows the potential for later deeper explorations. Secondly, we address several overlooked limitations of the conventional synchronization pipelines and propose the first DL based LiDAR-camera synchronization framework, which is an innovative self-supervised learning schema. Thirdly, the problem of Inertial Measurement Unit (IMU) denoising for navigation is studied, and we propose…
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/83874
note: Self-supervised Learning at the Sensor Layer in RoboticsbyKaiwen YuanB.Eng., Xi’an Jiaotong University, 2013M.A.Sc. The University of British Columbia, 2015A THESIS SUBMITTED IN PARTIAL FULFILLMENTOF THE REQUIREMENTS FOR THE DEGREE OFDoctor of PhilosophyinTHE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES(Electrical and Computer Engineering)The University of British Columbia(Vancouver)February 2023© Kaiwen Yuan, 2023The following individuals certify that they have read, and recommend to the Facultyof Graduate and Postdoctoral Studies for acceptance, the thesis entitled:Self-supervised Learning at the Sensor Layer in Roboticssubmitted by Kaiwen Yuan in partial fulfillment of the requirements for the degreeof Doctor of Philosophy in Electrical and Computer Engineering.Examining Committee:Z. Jane Wang, Professor, Electrical and Computer Engineering, UBCSupervisorSeptimiu (Tim) E. Salcudean, Professor, Electrical and Computer Engineering, UBCSupervisory Committee MemberHelge Rhodin , Assistant Professor, Computer Science, UBCSupervisory Committee MemberAntony J. Hodgson, Professor, Mechanical Engineering, UBCUniversity ExaminerPurang Abolmaesumi, Professor, Electrical and Computer Engineering, UBCUniversity ExamineriiAbstractModern robots are generally armed with diverse modalities of sensors, for variousfunctionalities and safety redundancy. The recent breakthrough of deep learning(DL) technologies demonstrates superior performance on many high-level tasks,especially with using multi-sensor fusion. While the majority of multi-modal DLmethods assume that sensors are well calibrated, synchronized and denoised, suchefforts at the sensor layer are non-trivial and get increasingly expensive with thegrowing complexity of robotic systems. Currently dominant approaches heavilyrely on specific hardware setup or high-end sensors, which generally are not cost-effective. This cost concern could be a bottleneck for the potential wide adoption oflow-cost robots in the near future. Eve…
has type: Thesis/Dissertation
date issued: 2023-05
is shown at: 10.14288/1.0427378
language: eng
degree discipline: Electrical and Computer Engineering
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
contributor: Wang, Z. Jane
type: Text
identifier URI: http://hdl.handle.net/2429/83874