With the completion of the Chinese space station,an increasing number of extravehicular activities will be executed by astronauts,which is regarded as one of the most dangerous activities in human space exploration.To...With the completion of the Chinese space station,an increasing number of extravehicular activities will be executed by astronauts,which is regarded as one of the most dangerous activities in human space exploration.To guarantee the safety of astronauts and the successful accomplishment of missions,it is vital to determine the pose of astronauts during extravehicular activities.This article presents a monocular vision-based pose estimation method of astronauts during extravehicular activities,making full use of the available observation resources.First,the camera is calibrated using objects of known structures,such as the spacesuit backpack or the circular handrail outside the space station.Subsequently,the pose estimation is performed utilizing the feature points on the spacesuit.The proposed methods are validated both on synthetic and semi-physical simulation experiments,demonstrating the high precision of the camera calibration and pose estimation.To further evaluate the performance of the methods in real-world scenarios,we utilize image sequences of Shenzhou-13 astronauts during extravehicular activities.The experiments validate that camera calibration and pose estimation can be accomplished solely with the existing observation resources,without requiring additional complicated equipment.The motion parameters of astronauts lay the technological foundation for subsequent applications such as mechanical analysis,task planning,and ground training of astronauts.展开更多
The rotation matrix estimation problem is a keypoint for mobile robot localization, navigation, and control. Based on the quaternion theory and the epipolar geometry, an extended Kalman filter (EKF) algorithm is propo...The rotation matrix estimation problem is a keypoint for mobile robot localization, navigation, and control. Based on the quaternion theory and the epipolar geometry, an extended Kalman filter (EKF) algorithm is proposed to estimate the rotation matrix by using a single-axis gyroscope and the image points correspondence from a monocular camera. The experimental results show that the precision of mobile robot s yaw angle estimated by the proposed EKF algorithm is much better than the results given by the image-only and gyroscope-only method, which demonstrates that our method is a preferable way to estimate the rotation for the autonomous mobile robot applications.展开更多
Joint calibration of sensors is an important prerequisite in intelligent driving scene retrieval and recognition. A simple and efficient solution is proposed for solving the problem of automatic joint calibration regi...Joint calibration of sensors is an important prerequisite in intelligent driving scene retrieval and recognition. A simple and efficient solution is proposed for solving the problem of automatic joint calibration registration between the monocular camera and the 16-line lidar. The study is divided into two parts: single-sensor independent calibration and multi-sensor joint registration, in which the selected objective world is used. The system associates the lidar coordinates with the camera coordinates. The lidar and the camera are used to obtain the normal vectors of the calibration plate and the point cloud data representing the calibration plate by the appropriate algorithm. Iterated closest points(ICP) is the method used for the iterative refinement of the registration.展开更多
稠密地图估计是同步定位与地图构建(Simultaneous Localization and Mapping,SLAM)的重要目标。针对经典的深度滤波算法重建精度不高的问题,提出一种基于逆深度滤波的改进单目稠密点云重建方法,在极线搜索阶段通过设置阈值提高效率,通...稠密地图估计是同步定位与地图构建(Simultaneous Localization and Mapping,SLAM)的重要目标。针对经典的深度滤波算法重建精度不高的问题,提出一种基于逆深度滤波的改进单目稠密点云重建方法,在极线搜索阶段通过设置阈值提高效率,通过逆深度高斯滤波器更新后验逆深度概率分布,通过帧内检测剔除外点。实验结果验证改进后的稠密重建算法具有更稠密、更精确的重建效果,且无须GPU加速。展开更多
基金supported by Hunan Provincial Natural Science Foundation for Excellent Young Scholars(Grant No.2023JJ20045)the Science Foundation(Grant No.KY0505072204)+1 种基金the Foundation of National Key Laboratory of Human Factors Engineering(Grant Nos.GJSD22006,6142222210401)the Foundation of China Astronaut Research and Training Center(Grant No.2022SY54B0605)。
文摘With the completion of the Chinese space station,an increasing number of extravehicular activities will be executed by astronauts,which is regarded as one of the most dangerous activities in human space exploration.To guarantee the safety of astronauts and the successful accomplishment of missions,it is vital to determine the pose of astronauts during extravehicular activities.This article presents a monocular vision-based pose estimation method of astronauts during extravehicular activities,making full use of the available observation resources.First,the camera is calibrated using objects of known structures,such as the spacesuit backpack or the circular handrail outside the space station.Subsequently,the pose estimation is performed utilizing the feature points on the spacesuit.The proposed methods are validated both on synthetic and semi-physical simulation experiments,demonstrating the high precision of the camera calibration and pose estimation.To further evaluate the performance of the methods in real-world scenarios,we utilize image sequences of Shenzhou-13 astronauts during extravehicular activities.The experiments validate that camera calibration and pose estimation can be accomplished solely with the existing observation resources,without requiring additional complicated equipment.The motion parameters of astronauts lay the technological foundation for subsequent applications such as mechanical analysis,task planning,and ground training of astronauts.
基金supported by National Natural Science Foundation of China (Nos. 60874010 and 61070048)Innovation Program of Shanghai Municipal Education Commission (No. 11ZZ37)+1 种基金Fundamental Research Funds for the Central Universities (No. 009QJ12)Collaborative Construction Project of Beijing Municipal Commission of Education
文摘The rotation matrix estimation problem is a keypoint for mobile robot localization, navigation, and control. Based on the quaternion theory and the epipolar geometry, an extended Kalman filter (EKF) algorithm is proposed to estimate the rotation matrix by using a single-axis gyroscope and the image points correspondence from a monocular camera. The experimental results show that the precision of mobile robot s yaw angle estimated by the proposed EKF algorithm is much better than the results given by the image-only and gyroscope-only method, which demonstrates that our method is a preferable way to estimate the rotation for the autonomous mobile robot applications.
文摘Joint calibration of sensors is an important prerequisite in intelligent driving scene retrieval and recognition. A simple and efficient solution is proposed for solving the problem of automatic joint calibration registration between the monocular camera and the 16-line lidar. The study is divided into two parts: single-sensor independent calibration and multi-sensor joint registration, in which the selected objective world is used. The system associates the lidar coordinates with the camera coordinates. The lidar and the camera are used to obtain the normal vectors of the calibration plate and the point cloud data representing the calibration plate by the appropriate algorithm. Iterated closest points(ICP) is the method used for the iterative refinement of the registration.
文摘稠密地图估计是同步定位与地图构建(Simultaneous Localization and Mapping,SLAM)的重要目标。针对经典的深度滤波算法重建精度不高的问题,提出一种基于逆深度滤波的改进单目稠密点云重建方法,在极线搜索阶段通过设置阈值提高效率,通过逆深度高斯滤波器更新后验逆深度概率分布,通过帧内检测剔除外点。实验结果验证改进后的稠密重建算法具有更稠密、更精确的重建效果,且无须GPU加速。
