# Non-Cooperative Spacecraft Pose Measurement with Binocular Camera and TOF Camera Collaboration
## 1 Introduction  
The data show that nearly 2000 objects larger than 10 cm in diameter have been found in low-Earth orbit [1]. Debris not only occupies valuable orbital resources but also increases the risk of collision with orbiting satellites, resulting in the removal of space debris becoming an urgent problem [2; 3; 4]. The concept of OOS was proposed in the 1960s, and a great deal of research work has been carried out in this field, with more than 130 missions launched [5; 6; 7; 8]. Since targets are in a state of free tumbling in space [9], it is necessary to determine the attitude of the target spacecraft in real time to provide accurate information for the guidance, navigation, and control (GNC) system [10], which is an indispensable condition for the acquisition of non-cooperative targets. Monocular cameras, binocular cameras, lidar and TOF cameras are commonly used sensors in short-range detection [11]. Acquiring non-cooperative target poses by means of stereo vision has been extensively studied. Numerous studies have accomplished pose estimation by identifying docking rings and other features of satellites [12; 13; 14; 15]. The disadvantage of an optical camera is that its imaging is greatly affected by illumination, which has certain limitations. The method based on point cloud estimation can overcome the influence of space's complex environment. [PERSON] et al. proposed a pose estimation method based on the known target model and the point cloud data generated by the lidar. The main advantage of this method is that the point cloud data are processed directly without the detection and tracking of features [16]. [PERSON] et al. designed an approach combining the principal component analysis and template matching. The authors focused on its ability to succeed in the measurement task without any initial guess work [17]. [PERSON] et al. presented a pose initialization based on template matching with sparse point cloud input, mainly concentrating on offline template construction [18]. However, the premiseof these methods requires the CAD model on the ground experiment to achieve the pose measurement of the target satellite. In addition, due to the need for a good initial value in the point cloud registration and a weaker echo signal when the target moves at a higher speed, it is difficult to solve the problem of pose estimation based on point clouds. It is a challenge for a single sensor to reliably track a target in real time, which demonstrates the necessity of studying the pose measurement method based on multisensor collaboration. In terms of active and passive means of collaborative navigation, [PERSON] et al. combined stereo vision and the ICP algorithm to estimate the motion of space debris. Their work mainly focused on using time series images to cope with the disadvantages of the ICP algorithm [19]. [PERSON] et al. designed a method to simply fuse the point cloud data reconstructed by stereo vision and the point cloud scanned through the laser radar. The extended Kalman filter algorithm was generated to acquire the pose and velocity of the non-cooperative target. However, the real-time performance needed to be improved [20]. [PERSON] et al. proposed a target recognition algorithm based on information fusion with binocular vision and laser radar, and the attitude measurement's accuracy was improved through a simulation experiment; however, the simulation model in this article is too simple [21]. [PERSON] et al. proposed an accurate pose estimation method for a non-cooperative target based on a TOF camera coupled with a grayscale camera. This major work is based on 2D line and 3D line correspondence. However, some salient feature points of the target model are not considered [22]. [PERSON] et al. presented a pose tracking method for on-orbit uncooperative targets based on the deep fusion of an optical camera and laser. The authors concentrated on acquiring a dense point cloud with scale information. However, the author does not mention the elimination of cumulative errors in point cloud registration [23].