Accuracy Investigation of the Pose Determination of a VR System

The usage of VR gear in mixed reality applications demands a high position and orientation accuracy of all devices to achieve a satisfying user experience. This paper investigates the system behaviour of the VR system HTC Vive Pro at a testing facility that is designed for the calibration of highly accurate positioning instruments like geodetic total stations, tilt sensors, geodetic gyroscopes or industrial laser scanners. Although the experiments show a high reproducibility of the position readings within a few millimetres, the VR system has systematic effects with magnitudes of several centimetres. A tilt of about 0.4° of the reference plane with respect to the horizontal plane was detected. Moreover, our results demonstrate that the tracking algorithm faces problems when several lighthouses are used.

An Improved Deep Keypoint Detection Network for Space Targets Pose Estimation

The on-board pose estimation of uncooperative target is an essential ability for close-proximity formation flying missions, on-orbit servicing, active debris removal and space exploration. However, the main issues of this research are: first, traditional pose determination algorithms result in a semantic gap and poor generalization abilities. Second, specific pose information cannot be accurately known in a complicated space target imaging environment. Deep learning methods can effectively solve these problems; thus, we propose a pose estimation algorithm that is based on deep learning. We use keypoints detection method to estimate the pose of space targets. For complicated space target imaging environment, we combined the high-resolution network with dilated convolution and online hard keypoint mining strategy. The improved network pays more attention to the obscured keypoints, has a larger receptive field, and improves the detection accuracy. Extensive experiments have been conducted and the results demonstrate that the proposed algorithms can effectively reduce the error rate of pose estimation and, compared with the related pose estimation methods, our proposed model has a higher detection accuracy and a lower pose determination error rate in the speed dataset.

ROBUST RESECTION MODEL FOR ALIGNING THE MOBILE MAPPING SYSTEMS TRAJECTORIES AT DEGRADED AND DENIED URBAN ENVIRONMENTS

Mobile mapping systems MMS equipped with cameras and laser scanners are widely used nowadays for different geospatial applications with centimetric accuracy either in project wise or national wise scales. The achieved positioning accuracy is very much related to the navigation unit, namely the GNSS and IMU onboard. Accordingly, in GNSS denied and degraded environments, the absolute positioning accuracy is worsened to few meters in some cases. Frequently, ground control points GCPs of a high positioning accuracy are used to align the MMS trajectories and to improve the accuracy when needed.The best way to integrate the MMS trajectories to the GCPs is by measuring them on the MMS images where the positioning accuracy is dropped. MMS images are mostly spherical panoramic (equirectangular) images and sometimes perspective and, in both types, it is required to precisely determine the images orientation in what is called as space resection or camera pose determination. For perspective images, the pose is conventionally determined by collinearity equations or by using projection and fundamental matrices. Whereas for equirectangular panoramic images it is based on resecting vertical and horizontal angles. However, there is still a challenge in the state–of–the–art of image pose determination because of the model nonlinearity and the sensitivity to proper initialization and spatial distribution of the points.In this research, a generic method is presented to solve the pose resection problem for the perspective and equirectangular images using oblique angles. The oblique angles are derived from the measured image coordinates and based on spherical trigonometry rules and vector geometry. The developed algorithm has proven to be highly stable and steadily converge to the global minimum. This is related to the robust geometric constraint offered by the oblique angles that are enclosed between the object points and the camera. As a result, the MMS trajectories are realigned accurately to the GCPs and the absolute accuracy is highly refined. Four experimental tests are presented where the results show the efficiency of the proposed angular based model in different cases of simulated and real data with different image types.

Relative pose determination of uncooperative known target based on extracting region of interest

This paper deals with the problem of relative pose determination between a model-known uncooperative space target and a chaser spacecraft using three-dimensional point cloud. A novel, reliable and real-time relative pose determination framework is proposed, which is composed of a region of interest–based initial pose acquisition method and a template matching iterative closest point algorithm for tracking pose. The initial pose is obtained by the principal component analysis method which aligns the region of interest extracted from the scanning point cloud with the region of interest of the known model point cloud, and a three-dimensional convex hull is constructed to extract the region of interest fast. To improve the iterative closest point, the registration between the template point cloud generated by the pose of last frame and the point cloud of the current frame is used to replace the registration between the local scanning point cloud and the global model point cloud. In addition, the performance (stability, low cost and their robustness against noise) of the proposed initial pose acquisition method and the accuracy and reliability of the pose tracking algorithm have been demonstrated by the numerical experiments. Finally, the effectiveness of the proposed method is verified by the field experiment.

Monocular Vision-Based Pose Determination in Close Proximity for Low Impact Docking

Pose determination in close proximity is critical for space missions in which monocular vision is one of the most promising solutions. Although numerous approaches such as using artificial beacons or specific shapes on spacecrafts have proved to be effective, the high individuation and the large time delay limit their use in low impact docking. This paper proposes a unified framework to determinate the relative pose between two docking mechanisms by treating their guide petals as measurement objects. Fusing the pose information of one docking mechanism to simplify image processing and creating an intermediate coordinate system to solve the perspective-n-point problem greatly improve the real-time performance and the robustness of the method. Experimental results show that the position measurement error is within 3.7 mm, while the rotation error around docking direction is less than 0.16°, corresponding to a measurement time reduction of 85%.