Capturing the motion of humans without any markers in a moving scene is a challenging issue. Typical methods, such as those based on fixed cameras or wearable sensors, have limits on the measurement range and the number of targets. Therefore, we propose a markerless motion capture technique to estimate the 3D pose of humans through a multi-UAV system. By utilizing the advantage of the large observation baselines between UAVs in the system, an algorithm to estimate the pose of airborne cameras and establish the sparse points cloud of background in overlapping view simultaneously was presented. A 3σ rule based on reprojection error is employed to eliminate the incorrect conjugate keypoint pairs, enhancing the precision of pose estimation for airborne cameras. To verify the accuracy of the developed technique, an indoor test was conducted to compare it with a commercial capture system, the results show that the average measurement error of the 3D joints of humans is less than 30mm. Furthermore, we demonstrate the feasibility of our approach in outdoor scenes, including multi-person scenarios and moving scenarios.

Space exploration missions involve significant participation from astronauts. Therefore, it is of great practical importance to assess the astronauts’ performance via various parameters in the cramped and weightless space station. In this paper, we proposed a calibration-free multi-view vision system for astronaut performance capture, including two modules: (1) an alternating iterative optimization of the camera pose and human pose is implemented to calibrate the extrinsic camera parameters with detected 2D keypoints. (2) Scale factors are restricted by the limb length to recover the real-world scale and the shape parameters are refined for subsequent postural reconstruction. These two modules can provide effective and efficient motion capture in a weightless space station. Extensive experiments using public datasets and the ground verification test data demonstrated the accuracy of the estimated camera pose and the effectiveness of the reconstructed human pose.

For a multi-mode Earth observation satellite carrying a line array camera and a multi-beam line array LiDAR, the relative installation attitude of the two sensors is of great significance. In this paper, we propose an on-orbit calibration method for the relative installation attitude of the camera and the LiDAR with no need for the calibration field and additional satellite attitude maneuvers. Firstly, the on-orbit joint calibration model of the relative installation attitude of the two sensors is established. However, there may exist a multi-solution problem in the solving of the above model constrained by non-ground control points. Thus, an alternate iterative method by solving the pseudo-absolute attitude matrix of each sensor in turn is proposed. The numerical validation and simulation experiments results show that the relative positioning error of the line array camera and the LiDAR in the horizontal direction of the ground can be limited to 0.8 m after correction by the method in this paper.

UAVs of various shapes and sizes have rapidly emerged, creating an urgent need for a highly transferable, fast, and robust 6D pose estimation method for visual tasks such as UAV swarming and monitoring. To address this, this paper presents a robust method for estimating the 6D pose of a UAV relative to an airborne camera based on hybrid features. Firstly, we employ KAPAO3D to simultaneously obtain pose objects using 3D losses with implicit keypoint associations and keypoint objects predicted based on explicit semantic information. Leveraging prior pose object knowledge enables the establishment of precise 2D-3D correspondences, facilitating the creation of fusion objects that alleviate missed detections and false positives in keypoint object predictions. Secondly, pose objects and fusion objects are respectively utilized to construct point and edge constraints to obtain an initial pose. We minimize the 3D errors of hybrid features with predicted confidence scores to further improve accuracy. Experimental results demonstrate that our method adapts to various types of UAVs and achieves superior performance, with higher ADD and Te metrics compared to other methods and a running speed of 30fps. This method is expected to have potential practical applications in high-precision real-time tracking tasks.

Image-based rotary sensors have the advantages of being simple and low cost and exhibit good robustness to harsh conditions. Existing image-based sensors calculate angle positions by mark detection with low angle-positioning precision. Image matching methods offer high pixel-positioning accuracy and can be used for object-positioning in images, but no image matching method has yet been developed for rotary angle estimation. In this article, an image matching algorithm—namely, the rotary angle Gauss–Newton (RAGN) algorithm—was proposed to accurately calculate the rotation angle. In addition, a measurement technique—called rotary digital image grating (RDIG)—was also proposed. The RDIG method was used to estimate the approximate angles based on the positions of four circles in the image pattern and measure the angles accurately using the RAGN algorithm. The measuring system was simple and required no high-precision fabrication. Experiments showed that the accuracy of the RDIG method was approximately 1.20 arcsecs (RMSE) with a rotary diameter of 50 mm, which is 4.7 times better than the accuracy of existing monocular rotary sensors.

Non-rigid point cloud registration holds significant importance for human body pose analysis in the fields of sports, medicine, gaming, etc. In this paper, we propose a non-rigid point cloud registration algorithm based on geodesic distance measurement, which can improve the accuracy of the registration for matching point pairs during non-rigid deformations. Firstly, a graph is constructed for two sets of point clouds using geodesic distance measurement considering that geodesic distance changes minimally during non-rigid deformation of the human body, which can preserve the point cloud matching information between corresponding points. Furthermore, a maximal clique search is employed to find combinations of matching pairs between point clouds. Finally, by driving the human body model parameters, sparse matching pairs are overlapped as much as possible to achieve non-rigid point cloud registration of the human body. The accuracy of the proposed algorithm is verified with FAUST and CAPE datasets.