Rectangular-structure-based pose estimation method for non-cooperative rendezvous

Efficient pose and motion estimation of non-cooperative target based on LiDAR

In on-orbit servicing missions, autonomous close proximity operations require knowledge of the target's pose and motion parameters. Due to the lack of prior information about the non-cooperative target in an unknown environment, the pose and motion estimation of an uncooperative target is a challenging task. In this paper, a relative position and attitude estimation method is proposed using consecutive point clouds. First, a fast plane detection method is used to extract the global features of non-cooperative targets. Compared with some other local feature-detection methods, the method mentioned in this paper is faster. Then a two-stage angle adjustment method and iterative closest point algorithm are used to register the two adjacent point clouds. Finally, an unscented Kalman filter is designed to estimate the relative pose and motion parameters (velocity and angular velocity) of the target. Experiments show that the proposed measurement method of pose and motion parameters has acceptable accuracy and good stability.

Relative Pose Determination of Uncooperative Spacecraft Based on Circle Feature

This paper investigates the problem of spacecraft relative navigation with respect to an unknown target during the close-proximity operations in the on-orbit service system. The serving spacecraft is equipped with a Time-of-Flight (ToF) camera for object recognition and feature detection. A fast and robust relative navigation strategy for acquisition is presented without any extra information about the target by using the natural circle features. The architecture of the proposed relative navigation strategy consists of three ingredients. First, a point cloud segmentation method based on the auxiliary gray image is developed for fast extraction of the circle feature point cloud of the target. Secondly, a new parameter fitting method of circle features is proposed including circle feature calculation by two different geometric models and results’ fusion. Finally, a specific definition of the coordinate frame system is introduced to solve the relative pose with respect to the uncooperative target. In order to validate the efficiency of the segmentation, an experimental test is conducted based on real-time image data acquired by the ToF camera. The total time consumption is saved by 94%. In addition, numerical simulations are carried out to evaluate the proposed navigation algorithm. It shows good robustness under the different levels of noises.

Band contour-extraction method based on conformal geometrical algebra for space tumbling targets

To remove space debris actively, the key step is choosing a suitable capturing method. The size characteristics of space debris are an important factor that affects the capturing method. This paper presents a contour-extracting algorithm for rolling targets, such as space debris. The representation of the point in Euclidean space is converted to a conformal space to obtain candidate contour points through the computational geometric and topological relationships among the points, lines, and surfaces in a high-dimensional space. By combining this conversion with the multiview stitching algorithm, the complete candidate contour point model is obtained. The final contour points are extracted by determining the concave and convex points in conformal space. Simulation results prove that the unified expression of the geometric features in conformal space not only reduces the computational complexity but also increases the speed of the contour point extraction. The method proposed in this paper is superior to other methods in terms of measurement accuracy, speed, and robustness; in particular, the accuracy increased by 60%.

Research on pose estimation for stereo vision measurement system by an improved method: uncertainty weighted stereopsis pose solution method based on projection vector

We present UWSPSM, an algorithm of uncertainty weighted stereopsis pose solution method based on the projection vector which to solve the problem of pose estimation for stereo vision measurement system based on feature points. Firstly, we use a covariance matrix to represent the direction uncertainty of feature points, and utilize projection matrix to integrate the direction uncertainty of feature points into stereo-vision pose estimation. Then, the optimal translation vector is solved based on the projection vector of feature points, as well the depth is updated by the projection vector of feature points. In the absolute azimuth solution stage, the singular value decomposition algorithm is used to calculate the relative attitude matrix, and the above two stages are iteratively performed until the result converges. Finally, the convergence of the proposed algorithm is proved, from the theoretical point of view, by the global convergence theorem. Expanded into stereo-vision, the fixed relationship constraint between cameras is introduced into the stereoscopic pose estimation, so that only one pose parameter of the two images captured is optimized in the iterative process, and the two cameras are better bound as a camera, it can improve accuracy and efficiency while enhancing measurement reliability. The experimental results show that the proposed pose estimation algorithm can converge quickly, has high-precision and good robustness, and can tolerate different degrees of error uncertainty. So, it has useful practical application prospects.

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%.

Aircraft Pose Estimation Based on Geometry Structure Features and Line Correspondences

A robust and accurate aircraft pose estimation method is proposed in this paper. The aircraft pose reflects the flight status of the aircraft and accurate pose measurement is of great importance in many aerospace applications. This work aims to establish a universal framework to estimate the aircraft pose based on generic geometry structure features. In our method, line features are extracted to describe the structure of an aircraft in single images and the generic geometry features are exploited to form line groups for aircraft structure recognition. Parallel line clustering is utilized to detect the fuselage reference line and bilateral symmetry property of aircraft provides an important constraint for the extraction of wing edge lines under weak perspective projection. After identifying the main structure of the aircraft, a planes intersection method is used to obtain the 3D pose parameters based on the established line correspondences. Our proposed method can increase the measuring range of binocular vision sensors and has the advantage of not relying on 3D models, cooperative marks or other feature datasets. Experimental results show that our method can obtain reliable and accurate pose information of different types of aircraft.

Pose Estimation for Straight Wing Aircraft Based on Consistent Line Clustering and Planes Intersection

Aircraft pose estimation is a necessary technology in aerospace applications, and accurate pose parameters are the foundation for many aerospace tasks. In this paper, we propose a novel pose estimation method for straight wing aircraft without relying on 3D models or other datasets, and two widely separated cameras are used to acquire the pose information. Because of the large baseline and long-distance imaging, feature point matching is difficult and inaccurate in this configuration. In our method, line features are extracted to describe the structure of straight wing aircraft in images, and pose estimation is performed based on the common geometry constraints of straight wing aircraft. The spatial and length consistency of the line features is used to exclude irrelevant line segments belonging to the background or other parts of the aircraft, and density-based parallel line clustering is utilized to extract the aircraft’s main structure. After identifying the orientation of the fuselage and wings in images, planes intersection is used to estimate the 3D localization and attitude of the aircraft. Experimental results show that our method estimates the aircraft pose accurately and robustly.