# Motion Estimation of Non-Cooperative Space Objects Based on Monocular Sequence Images
## 1 Introduction  
With the continuous development of aerospace technology, faulty spacecraft on-orbit maintenance, malfunctioning satellite rescue, repairing and refueling, debris removal, asteroid exploration, etc., have become realistic problems to be solved. The motion estimation of these non-cooperative space objects is an important prerequisite for successfully completing the above missions; that is, achieving the autonomous relative navigation of non-cooperative space objects. To improve navigation accuracy, autonomous navigation based on multi-source information fusion is applied in a specific spacecraft using microwave radars, laser radars, optical sensors, etc., as navigation sensors [1, 2]. However, due to the complexity of fusion filtering algorithms and the constraints of the on-board computing ability, it is difficult to achieve real multi-source information fusion for normal satellites. As a standard configuration of a spacecraft, optical sensors have the advantages of full range applicability and rich information, which are ideal information sources for the autonomous navigation of space non-cooperative objects. According to the application scenarios, autonomous navigation based on optical sensors can be divided into four categories. The first is autonomous relative navigation based on binocular vision. [PERSON] [3] proposed a method of pose estimation and 3D reconstruction of non-cooperative space objects based on binocular vision. This method has been demonstrated using real world data acquired inside the ISS [4]. [PERSON] [5] proposed a method of motion and inertial parameter estimation of non-cooperative object on orbit using stereo vision. The measurement accuracy and range of binocular vision depend heavily on the baseline length and are limited by the size of the spacecraft itself. The method is only suitable for close range measurement, within a few meters, and cannot be applied to remote navigation in the range of a few hundred meters to tens of kilometers. The second is autonomous relative navigation based on monocular vision and terrain matching. NASA's Mars rovers, Curiosity [6] and Perseverance [7], have adopted imagematching technology based on the terrain model of the landing area to achieve obstacle avoidance and fixed-point landing. This kind of method needs to achieve a high-precision 3D model of known objects; it needs to carry out surround measurements on the object to obtain a large number of Multiview images and send them back to earth station for processing. This kind of method has a long process period and is primarily applied to missions with known object and boundary conditions. It is not suitable for fast relative navigation missions with unknown objects. The third is autonomous relative navigation based on monocular vision and artificial markers. In a Japanese asteroid exploration mission, Hayabusa 1 [8] and Hayabusa 2 [9] dropped luminous object markers to the surface of asteroids Itokawa and Ryugu and used a monocular optical camera to achieve relative position measurements. Due to the need to release artificial markers, this method is difficult to apply to failed satellites, space debris, etc. The fourth is autonomous relative navigation based on SLAM. [PERSON] [10] proposed a motion estimation method based on a particle filter and nonlinear optimization and applied it in Hayabusa 2. SLAM technology is mainly used in the field of ground mobile robots, and the scene is required to be static [11]. In a relative navigation application scene, the object and spacecraft are always in motion, and the traditional methods cannot be directly applied to the satellite. [PERSON] [12; 13; 14; 15; 16] proposed aggregation operators on triangular cubic fuzzy numbers, which can be applied to decision-making problems. This method has application prospects in SLAM.