The pose (position and attitude) measurement of space non-cooperative targets is one of the key technologies in on-orbit service. Traditional stereo-vision-based methods are easily affected by illumination conditions, while the 3D laser radar has good stability, but the point cloud is sparse. In this paper, a method based on 3D laser radar and stereo-vision information is proposed to measure the pose and estimate the motion for non-cooperative targets. The proposed method can not only adapt to the harsh lighting conditions, but also has better robustness and higher precision. Firstly, find the matching point corresponding to the binocular camera by using the stereo matching algorithm, and then calculate the three-dimensional coordinates of the matching points according to the least squares method. Combined with the fusion calibration result, the point cloud data acquired by the 3D laser radar is converted to the left camera frame. Secondly, integrate the point cloud data obtained by the above two sensors to obtain a complete point cloud model of the non-cooperative target. Thirdly, the obtained point cloud model is registered, and the rigid transformation matrix of two adjacent point clouds is solved. Then, the relative pose and velocity (linear velocity and angular velocity) of the non-cooperative target are estimated by the EKF(Extended Kalman Filter) algorithm. Finally, an experimental system was built. It consists of a UR5 robot, a satellite mockup, two cameras, a 3D laser radar and an API laser tracker (for evaluating the accuracy of measurement) was built. The experiment results verified the proposed method.

The relative pose (position and attitude) measurement of space non-cooperative targets is very important for on-orbit servicing activities, such as target tracking, approaching, and capturing. The traditional methods rarely consider the instability of feature extraction and image blurring caused by target tumbling. In this paper, a method based on the maximum outer contour recognition (MOCR) is proposed to measure the pose of the target. Different feature extraction algorithms can simultaneously achieve close- and long- range measurement tasks. Firstly, the trailing image is restored by the image enhancement method. Secondly, the “rough + fine” combination recognition method (RFCRM) is used for contour extraction and connected components labeling of the restored image, and the target feature extraction time is reduced to one-third of traditional methods. Further, the elliptical surface on the maximum outer contour(MOC) is fitted by the least squares method (LSM), and the ellipse parameters (i.e. the center position, the long- and short-axes, and the deflection angle) are extracted. The accuracy of the target recognition is improved. Thirdly, for the close-range measurement, based on the detected ellipse parameter, the pose of the non-cooperative target is solved by the binocular imaging algorithm of the space circle; for the long-range measurement, the contour centroid of the target is calculated by the detected MOC, and the position of the target is solved by the LSM. Moreover, the effectiveness of the method is verified by the OpenSceneGraph(OSG) numerical simulation system. Finally, an experimental system consisting of a binocular camera, a UR5 manipulator and a satellite mockup was built. The experimental results verified the proposed method.

Pose (position and attitude) measurement of a space non-cooperative target is very important for on-orbital servicing tasks, including malfunctioning target repairing, space debris removal, and so on. However, such targets are generally non-cooperative, i.e., no markers are mounted on it and there is no prior knowledge. Therefore, the identification and measurement of a non-cooperative target is very challenging. In this paper, we propose an efficient method to recognize the natural objects with circular or near-circular shapes on the target, reducing the computation load and improving accuracy. First, the geometry properties of a practical non-cooperative target are analyzed. A stereo vision system is correspondingly designed to measure the relative pose of the target. Second, the error sources and time-consuming factors of the traditional method are analyzed. Then, a solution concept is proposed. Third, the efficient method is detailed to solve the geometry equation and determine the pose information, reducing the calculation complexity and increasing the accuracy. The image pre-processing and target detecting algorithms are realized on FPGAs, further accelerating the calculation speed. Finally, we develop an experiment system and verify the proposed method through practical experiments. The experiment system is composed of satellite mockup, binocular camera, and high precision laser tracker. The experiment results show that the proposed method has high accuracy and efficiency.

In surgery, the robot can automatically adjust the endoscope to ensure the stability of the surgical image and reduce the burden on the doctor. It is becoming a research hotspot of current surgical assistance. The identification and localization of endoscopic instruments tips is the key to achieve efficient “doctor–robot” collaboration. However, traditional methods have the problems of poor real-time performance and insufficient information expression. This article proposes an autonomous recognition of multiple surgical instrument tips based on arrow object bounding box (OBB)-YOLO network prediction, the proposed method not only realizes the recognition and localization of key parts of the instrument, but also uses arrows to enhance the expression of critical information such as the tip angle under the premise of meeting the real-time requirements for medical scenes. First, based on the modified YOLOv3 model, the Arrow OBB-YOLO network used to predict Arrow OBB is built, and the arrows are constrained inside the bounding box (BB) through coordinate transformation. Second, the optimization expression of arrow errors can enhance the prediction effect of the model, and the designed step-by-step training optimization method significantly accelerates the convergence speed of the model. Then, based on the Arrow OBB model, a method for generating the tracking areas is proposed. Finally, the experimental results show that the proposed method can accurately identify and locate the tips of the instrument with 36.5 fps and can improve the recognition effect of the BB when adding new information expression, which lays the foundation for “doctor–robot” collaborative surgery.