We focus on the problem of timing errors in navigation camera as a case study in a broader problem of the effect of a timing error in cyber-physical systems. These systems rely on the requirement that certain things happen at the same time or certain things happen periodically at some period $T$. However, as these systems get more complex, timing errors can occur between the components thereby violating the assumption about events being simultaneous (or periodic).
We consider the problem of a surgical navigation system where optical markers detected in the 2D pictures taken by two cameras are used to localize the markers in 3D space. A predefined array of such markers, known as a reference element, is used to navigate the corresponding CAD model of a surgical instrument on patient's images. The cameras rely on the assumption that the pictures from both cameras are taken exactly at the same time. If a timing error occurs then the instrument may have moved between the pictures. We find that, depending upon the location of the instrument, this can lead to a substantial error in the localization of the instrument. Specifically, we find that if the actual movement is $δ$ then the observed movement may be as high as $5δ$ in the operating range of the camera. Furthermore, we also identify potential issues that could affect the error in case there are changes to the camera system or to the operating range.
Human-Robot Navigation using Event-based Cameras and Reinforcement Learning
Department of Electrical Engineering, Universidad de Chile（智利大学电气工程系）；Advanced Mining Technology Center (AMTC), Universidad de Chile（智利大学先进采矿技术中心）；Institute of Engineering Sciences, Universidad de O’Higgins（奥希金斯大学工程科学研究所）
Learning Active Camera for Multi-Object Navigation
South China University of Technology（华南理工大学）；Pazhou Laboratory（琶洲实验室）；Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）；Information Technology R&D Innovation Center of Peking University（北京大学信息技术研发创新中心）；MIT-IBM Watson AI Lab（麻省理工学院-IBM沃森人工智能实验室）；UMass Amherst（马萨诸塞大学阿默斯特分校）
Extrinsic Parameter Calibration for Line Scanning Cameras on Ground Vehicles with Navigation Systems Using a Calibration Pattern
The University of Sydney（悉尼大学）；The Australian Centre for Field Robotics（澳大利亚现场机器人中心）
线扫描相机，其仅捕获单个像素线，已经越来越多地用于基于地面的移动或机器人平台。在将相机数据直接地理参考到世界坐标是有利的应用中，需要相机的6D姿态的准确估计。本文重点介绍了常见的情况下，移动平台配备了一个刚性安装的线扫描相机，其姿态是未知的，和导航系统提供车身姿态估计。我们提出了一种新的方法，估计相机的姿态相对于导航系统。该方法涉及成像和手动标记具有明显可识别点的校准图案，从相机和导航系统数据中对这些点进行三角测量，并重新投影它们以计算可能性，该可能性被最大化以估计6D相机姿态。此外，马尔可夫链蒙特卡罗（MCMC）算法用于估计偏移的不确定性。在两个不同的平台上进行测试，该方法能够估计的姿态在0.06 m / 1.05 $^{\circ}$和0.18 m / 2.39 $^{\circ}$。我们还提出了几种方法来显示和解释的6D结果在一个人可读的方式。
AINS: Affordable Indoor Navigation Solution via Line Color Identification Using Mono-Camera for Autonomous Vehicles
Sukkur IBA University（苏库尔IBA大学）；IBA Community College, Naushahro Feroze（IBA社区学院（瑙谢罗费罗兹））；University of Genova（热那亚大学）；Kathir College of Engineering（卡蒂尔工程学院）
Expert Consensus on Criteria for the Automated Assessment of Laparoscopic Camera Navigation
University Medical Center Göttingen（哥廷根大学医学中心）；Fraunhofer Research Institution for Individualized and Cell-based Medical Engineering (IMTE)（弗劳恩霍夫个性化与细胞医学工程研究所）
DualVision ArthroNav: Investigating Opportunities to Enhance Localization and Reconstruction in Image-based Arthroscopy Navigation via External Cameras
University of Arkansas（阿肯色大学）；Arthrex Inc.（阿瑟克斯公司）
Towards Increasing the Robustness of Predictive Steering-Control Autonomous Navigation Systems Against Dash Cam Image Angle Perturbations Due to Pothole Encounters
ICPS-net: An End-to-End RGB-based Indoor Camera Positioning System using deep convolutional neural networks
Faculty of Media Technology and Engineering, Iran Broadcasting University（伊朗广播大学媒体技术与工程学院）；Alpha Reality, AR/VR Solution Company（Alpha Reality AR/VR解决方案公司）
3D Visual Perception for Self-Driving Cars using a Multi-Camera System: Calibration, Mapping, Localization, and Obstacle Detection
University of California Berkeley（加州大学伯克利分校）；DSO National Laboratories（新加坡国防科技研究院）；National University of Singapore（新加坡国立大学）；Graz University of Technology（格拉茨技术大学）；ETH Zürich（苏黎世联邦理工学院）；Microsoft（微软）
Balgrist University Hospital, University of Zurich（苏黎世大学巴尔格利斯特大学医院）；ETH Zurich（苏黎世联邦理工学院）；OR-X Translational Center for Surgery, Balgrist University Hospital, University of Zurich（苏黎世大学巴尔格利斯特大学医院OR-X外科转化中心）；Technical University Munich（慕尼黑工业大学）
High-precision visual navigation device calibration method based on collimator
College of Aerospace Science and Engineering, National University of Defense Technology（国防科技大学航天科学与工程学院）；North Navigation Control Technology Co., Ltd（北方导航控制技术股份有限公司）
WorldCraft: From Camera Navigation to Object Manipulation in Interactive Video World Models
The Hong Kong University of Science and Technology（香港科学与技术大学）；AI Technology Center, Tencent Video, Tencent（腾讯视频AI技术中心，腾讯）；Wuhan University（武汉大学）；Peking University（北京大学）
Head Pose Estimation and 3D Neural Surface Reconstruction via Monocular Camera in situ for Navigation and Safe Insertion into Natural Openings
The Chinese University of Hong Kong（香港中文大学）；National University of Singapore（新加坡国立大学）；Shun Hing Institute of Advanced Engineering, The Chinese University of Hong Kong（香港中文大学舜兴高等工程研究所）；Shenzhen Research Institute, The Chinese University of Hong Kong（香港中文大学深圳研究院）
Traffic control using intelligent timing of traffic lights with reinforcement learning technique and real-time processing of surveillance camera images
Multicam-SLAM: Non-overlapping Multi-camera SLAM for Indirect Visual Localization and Navigation
School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University（上海交通大学电子信息与电气工程学院）；School of Mechanical and Power Engineering, East China University of Science and Technology（华东理工大学机械与动力工程学院）
NeuroLoc: Encoding Navigation Cells for 6-DOF Camera Localization
Software Engineering Institute, East China Normal University（华东师范大学软件工程学院）；School of Geospatial Information, Information Engineering University（信息工程大学地理信息学院）；University of Shanghai for Science and Technology（上海科技大学）；Fujian (Quanzhou) Institute of Advanced Manufacturing Technology, China（福建（泉州）先进制造技术研究院）
Incremental Real-Time Multibody VSLAM with Trajectory Optimization Using Stereo Camera
International Institute of Information Technology, Hyderabad（海得拉巴国际信息技术学院）；Max Planck Institute For Intelligent Systems（马克斯·普朗克智能系统研究所）；The Robotics Institute（机器人研究所）；Queensland University of Technology（昆士兰理工大学）；University of Petroleum and Energy Studies（石油与能源研究大学）
