Collaborative Perception, Localization and Mapping for Autonomous Systems
1st Edition
Published on 13. November 2020
XII, 141 pages
978-981-15-8860-0 (ISBN)
Description
This book presents the breakthrough and cutting-edge progress for collaborative perception and mapping by proposing a novel framework of multimodal perception-relative localization-collaborative mapping for collaborative robot systems. The organization of the book allows the readers to analyze, model and design collaborative perception technology for autonomous robots. It presents the basic foundation in the field of collaborative robot systems and the fundamental theory and technical guidelines for collaborative perception and mapping. The book significantly promotes the development of autonomous systems from individual intelligence to collaborative intelligence by providing extensive simulations and real experiments results in the different chapters. This book caters to engineers, graduate students and researchers in the fields of autonomous systems, robotics, computer vision and collaborative perception.
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Yufeng Yue | Danwei Wang
Collaborative Perception, Localization and Mapping for Autonomous Systems
Book
11/2020
Springer
€139.09
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Persons
Yufeng Yue received the B.Eng. degree in automation from Beijing Institute of Technology, Beijing, China, in 2014, the Ph.D. degree from Nanyang Technological University, Singapore, in 2019. He was a visiting scholar with University of California, Los Angeles in 2019. He served as a research fellow with School of Electrical and Electronic Eng., Nanyang Technological University, Singapore, from 2018 to 2020. He is currently an associate professor with School of Automation, Beijing Institute of Technology, Beijing, China. His research interests include perception, mapping and navigation for collaborative autonomous systems in complex environments.
Danwei Wang is leading the autonomous mobile robotics research group. He received his Ph.D and MSE degrees from the University of Michigan, Ann Arbor in 1989 and 1984, respectively. He received his B.E degree from the South China University of Technology, China in 1982. Since 1989, he has been with the School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore. Currently, he is professor and the co-director of the ST Engineering - NTU Corporate Laboratory. He is a senator in NTU Academics Council. He has served as general chairman, technical chairman and various positions in international conferences, such as ICARCV and IROS conferences. He is an associate editor for the International Journal of Humanoid Robotics and served as an associate editor of Conference Editorial Board, IEEE Control Systems Society from 1998 to 2005. He was a recipient of Alexander von Humboldt fellowship, Germany. His research interests include robotics, control theory and applications.
Danwei Wang is leading the autonomous mobile robotics research group. He received his Ph.D and MSE degrees from the University of Michigan, Ann Arbor in 1989 and 1984, respectively. He received his B.E degree from the South China University of Technology, China in 1982. Since 1989, he has been with the School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore. Currently, he is professor and the co-director of the ST Engineering - NTU Corporate Laboratory. He is a senator in NTU Academics Council. He has served as general chairman, technical chairman and various positions in international conferences, such as ICARCV and IROS conferences. He is an associate editor for the International Journal of Humanoid Robotics and served as an associate editor of Conference Editorial Board, IEEE Control Systems Society from 1998 to 2005. He was a recipient of Alexander von Humboldt fellowship, Germany. His research interests include robotics, control theory and applications.
Content
Introduction.- Technical Background.- Point Registration Approach for Map Fusion.- Submap-Based Probabilistic Inconsistency Detection.- Hierarchical Map Fusion Framework with Homogeneous Sensors.- Collaborative 3D Mapping using Heterogeneous Sensors.- All-Weather Collaborative Mapping with Dynamic Objects.- Collaborative Probabilistic Semantic Mapping using CNN.
