Autorentext

Junzhi Yu is a professor in the Department of Advanced Manufacturing and Robotics, College of Engineering, Peking University, China, and also a guest researcher at the Institute of Automation, Chinese Academy of Sciences. His research interests include intelligent robots, motion control, and intelligent mechatronic systems. He has authored or co-authored seven monographs and published more than 200 science citation index papers in prestigious robotics and automation-related journals. He has successively been listed among the Most Cited Researchers in China between 2014 and 2023. He is a fellow of the Institute of Electrical and Electronics Engineers.

Zhiqiang Cao is currently a professor at the State Key Laboratory of Multimodal Artificial Intelligence Systems from the Institute of Automation, Chinese Academy of Sciences. His current research interests include service robot and intelligent robot.

Peiyu Guan is currently an assistant professor at the State Key Laboratory of Multimodal Artificial Intelligence Systems from the Institute of Automation, Chinese Academy of Sciences. Her research interests include service robots, visual localization and mapping, and computer vision.

Chengpeng Wang is at the State Key Laboratory of Multimodal Artificial Intelligence Systems from the Institute of Automation, Chinese Academy of Sciences. His research interests include intelligent robot, robot localization and navigation.

Klappentext

Localization and mapping play a critical role in the autonomous task execution of mobile robots. This book covers the theoretical and technological aspects of robot localization and mapping, including visual localization and mapping, visual relocalization, LiDAR localization and mapping, and place recognition.

Inhalt

1 Introduction 2 Mathematical Foundation of Localization and Mapping Theory 3 Real-time Semantic Visual SLAM with Points and Objects 4 Visual Relocalization from the Perspective of Scene Coordinate Regression Network 5 Visual Relocalization from the Perspective of Place Recognition 6 Robot Visual Localization Framework Based on Offline Hybrid Map 7 Hierarchical LiDAR Odometry via Maximum Likelihood Estimation with Tightly Associated Distributions 8 Hierarchical Distribution-based Tightly-Coupled LiDAR Inertial Odometry 9 LiDAR Place Recognition Based on Range Image and Column-Shift-Invariant Attention 10 Summary and Outlook