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Hybrid Sensor Fusion for Unmanned Ground Vehicle This document is downloaded from DR‑NTU (https://dr.ntu.edu.sg) Nanyang Technological University, Singapore. Hybrid sensor fusion for unmanned ground vehicle Guan, Mingyang 2020 Guan, M. (2020). Hybrid sensor fusion for unmanned ground vehicle. Doctoral thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/144485 https://doi.org/10.32657/10356/144485 This work is licensed under a Creative Commons Attribution‑NonCommercial 4.0 International License (CC BY‑NC 4.0). Downloaded on 09 Oct 2021 04:08:30 SGT HYBRID SENSOR FUSION FOR UNMANNED GROUND VEHICLE MINGYANG GUAN School of Electrical & Electronic Engineering A thesis submitted to the Nanyang Technological University in partial fulfillment of the requirements for the degree of Doctor of Philosophy 2020 Statement of Originality I hereby certify that the work embodied in this thesis is the result of original research, is free of plagiarised materials, and has not been submitted for a higher degree to any other University or Institution. 25-03-2020 . Date Mingyang Guan Supervisor Declaration Statement I have reviewed the content and presentation style of this thesis and declare it is free of plagiarism and of sufficient grammatical clarity to be examined. To the best of my knowledge, the research and writing are those of the candidate except as acknowledged in the Author Attribution Statement. I confirm that the investigations were conducted in accord with the ethics policies and integrity standards of Nanyang Technological University and that the research data are presented honestly and without prejudice. 25-03-2020 . Date Changyun Wen Authorship Attribution Statement This thesis contains material from 2 papers published in the following peer-reviewed journal and conference, and 3 papers under reviewing where I was the first or joint first authors. Chapter 3 is published as Guan, M., Wen, C., Shan, M., Ng, C. L., and Zou, Y. Real- time event-triggered object tracking in the presence of model drift and occlusion. IEEE Transactions on Industrial Electronics, 66(3), 2054-2065 (2018). DOI: 10.1109/TIE.2018.2835390. The contributions of the co-authors are as follows: Prof. Wen provided the initial idea. I designed the event-triggered tracking framework and the algorithm of online object relocation. I co-designed the study with Prof Wen and performed all the experimental work at the ST-NTU Corplab. I also analyzed the data. Mr. Ng and Ms. Zhou assisted to collect the experimental results. I prepared the manuscript drafts. The manuscript was revised together with Prof. Wen and Dr. Shan. Chapter 4 is accepted as Guan, M., and Wen, C. Adaptive Multi-feature Reliability Re-determination Correlation Filter for Visual Tracking. IEEE Transactions on Multimedia. The contributions of the co-authors are as follows: Prof. Wen provided the initial idea. I co-designed the study with Prof Wen and performed all the experimental work at the ST-NTU Corplab. I proposed the tracking framework and two solutions on finding the reliability score of each feature. I prepared the manuscript drafts which were revised by Prof Wen. Chapter 5 is published as Song, Y*, Guan, M*, Tay, W.P., Law, C.L. and Wen, C., UWB/LiDAR Fusion For Cooperative Range-Only SLAM. IEEE International Conference on Robotics and Automation (ICRA), pp. 6568-6574, 2019 May. DOI: 10.1109/ICRA.2019.8794222. The authors with * are the jointly first author of this publication. The contributions of the co-authors are as follows: Prof. Wen suggested the idea on the fusion of UWB/LiDAR. I wrote the drafts related to LiDAR SLAM, and Dr. Song prepared the drafts related to UWB localization. I co-designed the fusion framework with Dr. Song. I designed and implemented all the experiments for the proposed method. The manuscript was revised together with Prof. Wen and Dr. Song. Dr. Song implemented the experiments related to the UWB localization. Prof. Tay and Prof. Law provided the advices on UWB sensors. Chapter 6 is under reviewing as Guan, M., Wen, C., Song, Y., and Tay, W.P., Autonomous Exploration Using UWB and LiDAR. IEEE Transactions on Industrial Electronics. The contributions of the co-authors are as follows: Prof. Wen suggested the idea of fusing UWB/LiDAR for autonomous exploration. I proposed a particle filter based step-by-step optimization framework to refine the state of robot and UWB beacons. I prepared the manuscript drafts. The manuscript was revised together with Prof. Wen and Dr. Song. Prof. Tay provided the advice on UWB sensors. I co-designed the study with Prof Wen and performed all the experimental work at the ST-NTU Corplab. Chapter 7 is under reviewing as Guan, M., Wen, C., and Song, Y. Autonomous Exploration via Region-aware Least-explored Guided Rapidly-exploring Random Trees. Journal of Field Robotics. The contributions of the co-authors are as follows: Prof. Wen suggested the idea of fusion UWB/LiDAR for autonomous exploration. I proposed a dual-UWB robot system and a least-explored guided RRTs for autonomous exploration. I prepared the manuscript drafts. The manuscript was revised together with Prof. Wen and Dr. Song. I co-designed the study with Prof Wen and performed all the experimental work at the ST-NTU Corplab. 25-03-2020 . Date Mingyang Guan Acknowledgements First of all, I wish to express my greatest gratitude and deepest appreciation to my advisor, Prof. Changyun Wen, for his continuous support, professional guidance and sincere encouragement throughout my PhD study. Prof. Wen's serious sci- entific attitude, rigorous scholarship and optimistic outlook on life, would always inspire me to work harder and live happier in the future. This thesis would not be possible without his brilliant ideas and extraordinary drive for research. Secondly, I would like to express my special thanks to Dr. Mao Shan, Dr. Zhe Wei, Dr. Zhengguo Li and Dr. Yang Song for their instructions, encouragement and assistance in my Ph.D research. When I began my Ph.D study, Mao helped me to quickly get familiar with the important technologies involving robotics. He always disscussed with me patiently to help me find solutions when I encountered problems. After Mao left NTU, Zhe has helped me to conqure some hard issues of the }Smart Wheelchair} project and analysis the experimental results. Later, Zhengguo has guided me a lot on problem formulating and solving, he also shared me some valuable knowledeges and research directions. During my last years of Ph.D study, Yang has helped me in both theoretical and experimental studies. We have had pleasant and encouraging discussions about both the project and my Ph.D study. Overall, their kind supports helped me overcome lots of difficulties during my PhD study. Thirdly, I want to thank my colleagues and friends, Dr. Xiucai Huang, Dr. Renjie He, Dr. Fanghong Guo, Dr. Fei Kou, Ms. Ying Zou, Dr. Lantao Xing, Mr. Ruibin Jing, Dr. Jie Ding, Dr. Jingjing Huang and Dr. Hui Gao who are in Prof. Wen's group, Dr. Yuanzhe Wang, Mr. Chongxiao Wang, Mr. Yijie Zeng, Mr. Mok Bo Chuan, Dr. Yunyun Huang, Mr. Yan Xu, Mr. Kok Ming Lee, Mr. Paul Tan, Mr. Pek Kian Huat Alex and Mr. Song Guang Ho who are in ST Engineering-NTU Corporate Laboratory, for the experience of studying and working with them, and also for their generous help in countless experiments. xi xii Last but not least, I would like to express my deepest thanks to my parents, my wife as well as other family members, for their endless love and unswerving support. Abstract The unmanned ground vehicles (UGVs) have been applied to execute many im- portant tasks in the real world scenarios such as surveillance, exploring the hazard environment and autonomous transportation. The UGV is a complex system as it is integrated by several challenge technologies, such as simultaneously localization and mapping (SLAM), collision-free navigation, and robotic perception. Gener- ally, the navigation and control of UGVs in the Global Positioning System (GPS) denied environment (i.e., indoor scenario) are critically dependent on the SLAM system which provides localization service for UGVs, while the robotic perception endows UGVs the ability of understanding their surrounding environments, such as continuously tracking the moving obstacles and then filtering them out in the localization process. In this thesis, we concentrate on the two topics involving autonomously robotic systems, say SLAM and visual object tracking. The first part of this thesis focuses on visual object tracking, which is to generally estimate the motion state of the given target based on its appearance informa- tion. Though many promising tracking models have been proposed in the recent decade, some challenges are still waiting to be addressed, such as computational efficiency, tracking model drift due to illumination variation, motion blur, occlusion and deformation. Therefore, we address these issues by proposing two trackers: 1) Event-triggered tracking (ETT) framework which attempts to enable the tracking task to be carried out by an efficient short-term tracker (i.e., correlation filter based tracker) in most of the time while triggers to restore the short-term tracker once it fails to track the target, thus a balance between tracking accuracy and efficiency is achieved; 2) reliability re-determinative correlation filter (RRCF) which aims to take advantages from multiple feature representations to robustify the tracking model. Meanwhile, we propose two different weight solvers to adaptively adjust the importance of each feature. Extensive experiments have been designed on several large datasets to validate that: 1) the proposed tracking framework is superior to enhance the robustness of tracking model, 2) the proposed two weight solvers can xiii xiv effectively find the optimal weight for each feature. As expected, the proposed two trackers indeed improve the accuracy and robustness compared to the state-of-the- art trackers.
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