空间碎片或者失效卫星往往绕其惯性主轴做自旋运动，虽然处于一种稳定状态，但是对其运动状态测量的难度相比三轴稳定目标而言又增加了许多，主要体现在复杂光场环境下和目标观测面周期性进出视场引起的特征点丢失、尺度变化和旋转变化，以及长时间连续观测引起的累积误差增大、位姿解算不收敛等难题。通过构建优化目标特征数据库，将传统测量方式中前后帧的匹配转变为当前帧与特征数据库的匹配和特征点的优化，即使中间过程帧出现了偏差仍然可以较好地跟踪后续图像帧的正确位置。在李代数空间下建立位姿向量微分扰动方程，获得测量值与估计值的残差目标函数，基于贝叶斯法则最大后验概率和李群与李代数的对指变换法则，求取位姿向量最优解。解决了李群空间下由于位姿变换矩阵不可加性而无法优化的问题，提高了连续测量过程中系统的测量精度。实验结果表明，无优化测量方法无法保证整个旋转周期的有效测量；通过增加位姿优化过程，连续稳定测量时间明显延长，针对以12（°）/s自旋运动的目标，测量稳定段误差在2°以内，旋转角速度测量误差为0.12（°）/s。
Space debris or failed satellites often spin around their main inertial axis. Despite their stable state, measurement of their motion state is more difficult than that of a three-axis stable target. Difficulties mainly involve loss of feature points, scale changes and rotation changes in a complex light field environment, and those caused by periodic entry and exit of the target observation surface, as well as increase of cumulative errors and non-convergence of the calculated position and attitude resulted from long-term continuous observation. An optimized target feature database is constructed to turn the matching of the front and back frames in the traditional measurement method into the matching and optimization of the feature points of the current frame and the feature database. In this way, even if an error occurs in the middle process frame, the correct position of the subsequent image frame can still be tracked fairly well. The differential perturbation equation of the pose vector in the Lie algebra space is established to obtain the residual objective function of the measured and estimated values. Based on the maximum posterior probability of the Bayes' rule and the pair-finger transformation rule of Lie group and Lie algebra, the optimal solution of the pose vector is obtained, thereby solving the non-optimizable problem in the Lie group space due to the non-additivity of the pose transformation matrix, and improving the measurement accuracy of the system in the continuous measurement process. Experimental results show that the unoptimized measurement method cannot guarantee effective measurement of the entire rotation cycle, and that increasing the pose optimization process significantly extends the time of continuous stable measurement. For targets with spin motion of 12 (°)/s, the measurement error of the stable segment is within 2°, and that of the rotation angular velocity is 0.12 (°)/s.
[1] DURRANT W H, BAILEY T. Simultaneous localization and mapping:Part I[J]. IEEE Robotics & Automation Magazine, 2006, 13(2):99-110.
[2] SONNENBURG A, TKOCZ M, JANSCHEK K. Ekf-slam based approach for spacecraft rendezvous navigation with unknown target spacecraft[J]. IFAC Proceedings Volumes, 2010, 43(15):339-344.
[3] AUGENSTEIN S, ROCK S M. Improved frame-to-frame pose tracking during vision only SLAM/SFM with a tumbling target[C]//Proceedings of the IEEE International Conference on Robotics and Automation. Piscataway:IEEE Robotics and Automation Society, 2011:3131-3138.
[4] SCHNITZER F, ANSCHEK K, WILLICH G. Experimental results for image-based geometrical reconstruction for spacecraft rendezvous navigation with unknown and uncooperative target spacecraft[C]//Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems. Piscataway:IEEE Robotics and Automation Society, 2012:5040-5045.
[5] BZY H, ESS A, TUYTELAARS T, et al. Speeded-up robust features[J]. Computer Vision and Image Understanding, 2008, 110:346-359.
[6] TWEDDLE B E. Computer vision-based localization and mapping of an unknown uncooperative and spinning target for spacecraft proximity operations[D]. Cambridge:Massachusetts Institute of Technology, 2013:72-77.
[7] KAESS M, RANGANATHAN A, DELLAERT F. ISAM:Incremental smoothing and mapping[J]. IEEE Transactions on Robotics, 2008, 24(6):1365-1378.
[8] SEGAL S, CARMI A, GURFIL P. Stereovision-based estimation of relative dynamics between non-cooperative satellites:theory and experiments[J]. IEEE Transactions on Control Systems Technology, 2014, 22(2):568-584.
[9] SEGAL S, GURFIL P. Effect of kinematic rotation-translation coupling on relative spacecraft translational dynamics[J]. Journal of Guidance Control & Dynamics, 2009, 32(3):1045-1050.
[10] CONWAY D, MACOMBER B, CAVALIERI K A, et al. Vision-based relative navigation filter for asteroid rendezvous[J]. Advances in the Astronautical Sciences, 2014, 151(1):25-33.
[11] PESCE V, LAVAGNA M, BEVILACQUA R. Stereovision-based pose and inertia estimation of unknown and uncooperative space objects[J]. Advances in Space Research, 2017, 59(1):236-251.
[12] 吴俊君. 移动机器人视觉同时定位与地图构建关键算法研究[D]. 广州:华南理工大学, 2013:22-24. WU J J. Key algorithms of visual simultaneous localization and mapping for mobile robot[D]. Guangzhou:South China University of Technology, 2013:22-24(in Chinese).
[13] RUBLEE E, RABAUD V, KONOLIGE K, et al. ORB:An efficient alternative to sift or surf[C]//Proceedings of the IEEE International Conference on Computer Vision. Barcelona:IEEE Computer Society, 2011:2564-2571.
[14] MICHAEL C, VINCENT L, CHRISTOPH S, et al. Brief:Binary robust independent elementary features[C]//Proceedings of the European Conference on Computer Vision. Heraklion:Springer Verlag, 2010:778-792.
[15] MUR-ARTAL R, MONTIEL J M, TARDOS J D. ORB-SLAM:A versatile and accurate monocular slam system[J]. IEEE Transactions on Robotics, 2015, 31(5):1147-1163.
[16] SCHLEICHER D, BERGASA L M. Real-time hierarchical stereo visual slam in large-scale environments[J]. Robotics and Autonomous Systems, 2010, 58(8):991-1002.
[17] GONG Y, MENG D, SEIBEL E J. Bound constrained bundle adjustment for reliable 3D reconstruction[J]. Optics Express, 2015, 23(8):10771-10786.
[18] GRISETTI G, KUMMERLE R, STACHNISS C, et al. A tutorial on graph-based slam[J]. IEEE Intelligent Transportation Systems Magazine, 2010, 2(4):31-43.
[19] LIU Y, ZHANG H. Indexing visual features:real-time loop closure detection using a tree structure[C]//Proceedings of the IEEE International Conference on Robotics and Automation. Piscataway:IEEE Robotics and Automation Society, 2012:3613-3618.
[20] LUK R, LAM W. Information retrieval:implementing and evaluating search engines[M]. Cambridge:Massachusetts Institute of Technology Press, 2010:120-128.
[21] 高翔, 张涛. 视觉SLAM十四讲从理论到实践[M]. 北京:电子工业出版社, 2017:64-76. GAO X, ZHANG T. Visual slam 14 lectures from theory to practice[M]. Beijing:Electronic Industry Press, 2017:64-76(in Chinese).
[22] 刘宗明, 曹姝清, 张宇, 等. 非合作航天器逆深度参数化姿态估计[J]. 光学精密工程, 2017, 25(2):451-459. LIU Z M, CAO S Q, ZHANG Y, et al. Inverse depth parametrization for attitude estimation of a non-cooperative spacecraft[J]. Optics and Precision Engineering, 2017, 25(2):451-459(in Chinese).
CJA