###### Abstract  
Accurately determining the attitude of non-cooperative spacecraft in on-orbit servicing (OOS) has posed a challenge in recent years. In point cloud-based spatial non-cooperative target attitude estimation schemes, high-precision point clouds, which are more robust to noise, can offer more accurate data input for three-dimensional registration. To enhance registration accuracy, we propose a noise filtering method based on moving least squares microplane projection (mpp-MLS). This method retains salient target feature points while eliminating redundant points, thereby enhancing registration accuracy. Higher accuracy in point clouds enables a more precise estimation of spatial target attitudes. For coarse registration, we employed the Random Sampling Consistency (RANSAC) algorithm to enhance accuracy and alleviate the adverse effects of point cloud mismatches. For fine registration, the J-ICP algorithm was utilized to estimate pose transformations and minimize spacecraft cumulative pose estimation errors during movement transformations. Semi-physical experimental results indicate that the proposed attitude parameter measurement method outperformed the classic ICP registration method. It yielded maximum translation and rotation errors of less than 1.57 mm and 0.071\({}^{\circ}\), respectively, and reduced maximum translation and rotation errors by 56% and 65%, respectively, thereby significantly enhancing the attitude estimation accuracy of non-cooperative targets. on-orbit service; ToF camera; point cloud; pose estimation; moving least squares +
Footnote †: journal:  
Current position estimation schemes utilizing 3D point cloud data are typically categorized into template matching based schemes and point cloud feature matching-based schemes. Template-based matching involves estimating position by matching a known target model, which is basically difficult to obtain in advance in practical applications. [PERSON] et al. [13] developed an online 3D template matching method and proposed a model-based attitude estimation approach [14]. [PERSON] et al. [15] introduced a lidar-based fusion depth map and point cloud for attitude tracking, utilizing the Iterative Closest Point (ICP) algorithm to align the simplified sparse point cloud with the known target model point cloud to determine relative attitude. In simulation experiments, translation error and rotation error were no more than 1 cm and 0.5\({}^{\circ}\), respectively. [PERSON] et al. [16] used point cloud data generated by the Flash Lidar sensor with a target model acquired in advance to estimate the relative attitude for a close-range satellite using dense point cloud data directly, although noise and artifacts were not considered in the experiment. Concerning the feature matching approach, effectively performing noise reduction on acquired target point cloud data is crucial for accurate matching. [PERSON] et al. [17] proposed a ToF-based attitude estimation scheme, detecting changes in the target's attitude at different time points by identifying spacecraft components like nozzles and fairings. However, only model data were used for validation in the experiments, leaving the performance in noisy environments unknown. [PERSON] et al. [18] utilized the ICP method for aligning two frames of a point cloud and introduced a point median filtering technique to eliminate poorly corresponding point pairs. However, this approach may not be suitable for tumbling targets or complex geometric targets with high rotational speeds due to the simplistic target geometry considered. [PERSON] et al. [19] leveraged the initial positional attitude values and key point information from stereo vision, obtained initial attitude data through joint calibration, and used the ICP algorithm for inter-frame alignment to determine the final attitude of the target. [PERSON] et al.