该文针对在轨卫星量测信息源的远距离定位问题, 提出一种利用星座空间构型特性的n点透视(perspective-n-point, PnP)问题改进定位方法： 从选取定位数据源出发, 分析卫星几何构型对定位误差的影响, 提取影响构型的主要参数, 建立几何构型评价函数, 评价星座构型的可用度。该文提出的PnP方法利用视场内构型筛选的星座卫星角度量测信息进行定位解算, 筛选出受图像噪声影响较小的构型, 提高定位解算精度, 并通过仿真试验验证了筛选方法的有效性。该文研究结果可为远距离光学导航定位问题提供参考。
Abstract
[Objective] As the number of low earth orbit(LEO) satellites increases, the applicability of optical navigation technology, which uses these satellites as optical information sources, is continually improving. When three or more satellites are simultaneously observed within the optical field of view, the perspective-n-point (PnP) positioning method can be used for pose estimation and positioning. The PnP problem was initially used primarily for camera calibration. Currently the applications of the PnP problem have expanded to various engineering tasks, including simultaneous visual localization and mapping, spatial noncooperative target pose estimation, and the critical stages of rendezvous and docking. However, the PnP algorithm struggles with low positioning accuracy over long distances. Therefore, it is necessary to study the spatial geometric positioning problem when observing satellites at relative distances exceeding hundreds of kilometers. [Methods] This study begins by selecting positioning data sources and statistically investigating the impact of constellation configuration within the optical field of view on positioning errors. The relationships between the constellation area and positioning errors, the geometric angle and positioning errors, and the relationship between the orbit height distribution and positioning errors are analyzed. Through this analysis, the primary influencing indicators are identified as the area, angle, and distance indicators. The distance indicator, in particular, represents the three-dimensional information of the configuration, which cannot be characterized by position dilution of precision (PDOP). To unify the measurement space of each indicator, the indicators are normalized, and the entropy weight method is used to calculate the weight of each indicator. An evaluation function for the constellation configuration is established to assess the configuration availability. The availability distribution is statistically analyzed to determine the evaluation criteria. Finally, using the calculated availability, configurations that are less affected by image noise are selected for the PnP pose calculation. In addition, the difference between the PDOP and positioning error is presented, and the PnP pose is estimated after the configuration is evaluated. [Results] Taking the P3P problem as an example, the positioning error was smaller when the distribution area of the three satellites was larger and more dispersed. According to the proposed screening method, positioning accuracy was improved by more than 50% compared with the situation without screening. Additionally, the configuration positioning accuracy was improved by approximately 37% compared with that of the PDOP-optimized configuration. [Conclusions] Constellation satellites enhance space navigation information sources. Configuration screening effectively improves the accuracy of PnP geometric positioning over long distances, thereby introducing a new concept for long-distance optical navigation.
关键词
星座构型 /
几何定位 /
多点透视问题 /
构型筛选
Key words
constellation configuration /
geometric positioning /
perspective-n-point problem /
configuration screening
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1] 王勇, 龙定央, 骆盛, 等. “星链”系统星座覆盖及应用分析 [J]. 中国航天, 2021(8): 43-47. WANG Y, LONG D Y, LUO S, et al. Constellation coverage and application analysis of Starlink system [J]. Aerospace China, 2021(8): 43-47. (in Chinese)
[2] PUMAROLA A, VAKHITOV A, AGUDO A, et al. PL-SLAM: Real-time monocular visual SLAM with points and lines [C]//IEEE International Conference on Robotics and Automation. Singapore, Singapore: IEEE, 2017: 4503-4508.
[3] MARCHAND E, UCHIYAMA H, SPINDLER F. Pose estimation for augmented reality: A hands-on survey [J]. IEEE Transactions on Visualization and Computer Graphics, 2016, 22(12): 2633-2651.
[4] XU J J, SONG B, YANG X, et al. An improved deep keypoint detection network for space targets pose estimation [J]. Remote Sensing, 2020, 12(23): 3857.
[5] 汪启跃, 王中宇. 基于单目视觉的航天器位姿测量 [J]. 应用光学, 2017, 38(2): 250-255. WANG Q Y, WANG Z Y. Position and pose measurement of spacecraft based on monocular vision [J]. Journal of Applied Optics, 2017, 38(2): 250-255. (in Chinese)
[6] LIU H Q, GUAN X J, LUO S B, et al. Vision inertial fusion based on BP neural network for aircraft autonomous positioning [C]//2020 International Conference on Guidance, Navigation and Control. Tianjin, China: Springer, 2020: 2905-2915.
[7] JOSPIN L, STOVEN-DUBOIS A, CUCCI D A. Photometric long-range positioning of LED targets for cooperative navigation in UAVs [J]. Drones, 2019, 3(3): 69.
[8] 刘崇亮, 刘垒, 扈光锋, 等. 一种着陆视觉导航P3P问题的解法 [J]. 导航定位与授时, 2018, 5(1): 64-67. LIU C L, LIU L, HU G F, et al. A P3P problem solving algorithm for landing vision navigation [J]. Navigation Positioning and Timing, 2018, 5(1): 64-67. (in Chinese)
[9] 刘建业. 导航系统理论与应用 [M]. 西安: 西北工业大学出版社, 2010. LIU J Y. Theory and application of navigation system [M]. Xi'an: Northwestern Polytechnical University Press, 2010. (in Chinese)
[10] ANSARI K, JAMJAREEGULGARN P. Effect of weighted PDOP on performance of linear Kalman filter for RTK drone data [J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19: 1-4.
[11] 朱林梅, 董秀成, 张征宇, 等. 面向奇异构型目标点分布的相机位姿估计算法 [J]. 光子学报, 2021, 50(7): 293-305. ZHU L M, DONG X C, ZHANG Z Y, et al. Camera pose estimation algorithm for singular configuration of target points [J]. Acta Photonica Sinica, 2021, 50(7): 293-305. (in Chinese)
[12] 洪洋, 孙秀霞, 王栋, 等. 基于矩形几何特性的小型无人机快速位姿估计方法 [J]. 中国激光, 2016, 43(5): 226-238. HONG Y, SUN X X, WANG D, et al. Fast pose estimation method for unmanned aerial vehicle based on rectangular geometry feature [J]. Chinese Journal of Lasers, 2016, 43(5): 226-238. (in Chinese)
[13] 王中宇, 李亚茹, 郝仁杰, 等. 基于点特征的单目视觉位姿测量算法 [J]. 红外与激光工程, 2019, 48(5): 169-176. WANG Z Y, LI Y R, HAO R J, et al. Monocular vision pose measurement algorithm based on points feature [J]. Infrared and Laser Engineering, 2019, 48(5): 169-176. (in Chinese)
[14] CHRISTIAN J A. Autonomous initial orbit determination with optical observations of unknown planetary landmarks [J]. Journal of Spacecraft and Rockets, 2019, 56(1): 211-220.
[15] 黄西尧, 何羿霆, 杜华军, 等. 地球同步轨道目标物深度学习检测方法 [J]. 宇航学报, 2021, 42(10): 1283-1292. HUANG X Y, HE Y T, DU H J, et al. Geostationary orbit object detection based on deep learning [J]. Journal of Astronautics, 2021, 42(10): 1283-1292. (in Chinese)
[16] 黎奇, 白征东, 黎琼, 等. 定位测姿系统室外三维动态检定场的几何设计 [J]. 清华大学学报(自然科学版), 2019, 59(11): 895-901. LI Q, BAI Z D, LI Q, et al. Geometric design of an outdoor three-dimensional kinematic verification field for a position and orientation system [J]. Journal of Tsinghua University (Science and Technology), 2019, 59(11): 895-901. (in Chinese)
[17] KIM Y, LEE D J, BANG H. Vision-only terrain referenced navigation and uncertainty analysis for unmanned aircraft [C]//AIAA Guidance, Navigation, and Control (GNC) Conference. Boston, USA: AIAA, 2013： 4696-4706.
[18] 高扬, 王梦丽, 楚恒林. 卫星导航信号畸变模型威胁空间减小方法 [J]. 清华大学学报(自然科学版), 2021, 61(9): 1008-1014. GAO Y, WANG M L, CHU H L. Threat space reduction method for satellite navigation signal distortion model [J]. Journal of Tsinghua University (Science and Technology), 2021, 61(9): 1008-1014. (in Chinese)
[19] KNEIP L, SCARAMUZZA D, SIEGWART R. A novel parametrization of the perspective-three-point problem for a direct computation of absolute camera position and orientation [C]//IEEE CVPR 2011. Colorado Springs, USA: IEEE, 2011: 2969-2976.
基金
国家自然科学基金优秀青年基金(12222202)
PDF(4795 KB)
