In this paper, we propose a method to estimate the exact location of a camera in a cyber-physical system using the exact geographic coordinates of four feature points stored in QR codes(Quick response codes) and the pixel coordinates of four feature points analyzed from the QR code images taken by the camera. Firstly, the P4P(Perspective 4 Points) algorithm is designed to uniquely determine the initial pose estimation value of the QR coordinate system relative to the camera coordinate system by using the four feature points of the selected QR code. In the second step, the manifold gradient optimization algorithm is designed. The rotation matrix and displacement vector are taken as the initial values of iteration, and the iterative optimization is carried out to improve the positioning accuracy and obtain the rotation matrix and displacement vector with higher accuracy. The third step is to convert the pose of the QR coordinate system with respect to the camera coordinate system to the pose of the AGV(Automated Guided Vehicle) with respect to the world coordinate system. Finally, the performance of manifold gradient optimization algorithm and P4P analytical algorithm are simulated and compared under the same conditions.One can see that the performance of the manifold gradient optimization algorithm proposed in this paper is much better than that of the P4P analytic algorithm when the signal-to-noise ratio is small.With the increase of the signal-to-noise ratio,the performance of the P4P analytic algorithm approaches that of the manifold gradient optimization algorithm.when the noise is same,the performance of manifold gradient optimization algorithm is better when there are more feature points.
Towards Edge Intelligence via Autonomous Navigation: A Robot-Assisted Data Collection Approach
College of Information Science and Technology, Jinan University（暨南大学信息科学技术学院）；College of Physics and Information Engineering, Fuzhou University（福州大学物理与信息工程学院）；School of Electronics and Communication Engineering, Guangzhou University（广州大学电子与通信工程学院）；School of Computer Science, Beijing University of Posts and Telecommunications（北京邮电大学计算机学院）
Department of Computer Science and Engineering, University of Michigan, USA（计算机科学与工程系，密歇根大学）；Department of Computer and Information Sciences, University of Delaware, USA（计算机与信息科学系，特拉华大学）
EnQuery: Ensemble Policies for Diverse Query-Generation in Preference Alignment of Robot Navigation
University of Bonn（波恩大学）；Center for Robotics at the University of Bonn（波恩大学机器人中心）；Lamarr Institute for Machine Learning and Artificial Intelligence（拉马尔机器学习与人工智能研究所）
PythonRobotics: a Python code collection of robotics algorithms
University of California, Berkeley（加州大学伯克利分校）；Appalachian State University（阿巴拉契亚州立大学）；inVia Robotics, Inc.（inVia机器人公司）；Skyryse Inc.（Skyryse公司）；ENSTA ParisTech（巴黎高等先进技术学院）；Unmanned Life（无人生活公司）
Co-training with Ego-centric Video and Demonstration for Robot Navigation Task
Department of Informatics, Graduate School of Informatics, Kyoto University（信息学系，京都大学研究生院）；Spatial Robotics Research Center, Fujitsu Limited（空间机器人研究中心，富士通有限公司）
FlashNav: Training Deployable Robot Navigation Policies in Seconds
Eastern Institute of Technology, Ningbo（宁波东方理工大学）；The Hong Kong Polytechnic University（香港理工大学）；National University of Singapore（新加坡国立大学）；University of Science and Technology of China（中国科学技术大学）；Shanghai Jiao Tong University（上海交通大学）
Bi3: A Biplatform, Bicultural, Biperson Dataset for Social Robot Navigation
Department of Robotics, University of Michigan（机器人系，密歇根大学）；LAAS-CNRS, University of Toulouse（LAAS-CNRS，图卢兹大学）；INRIA, University of Lorraine（INRIA，洛林大学）
ConvXformer: Differentially Private Hybrid ConvNeXt-Transformer for Inertial Navigation
School of Computing, Korea Advanced Institute of Science and Technology（韩国科学技术院计算机科学学院）；School of Information Technology, Deakin University（德金大学信息科技学院）；Department of Computer Science and Technology, University of Cambridge（剑桥大学计算机科学与技术系）
