A multi-constraints based pose coordination model for large volume components assembly

In the assembly process of large volume product, engineering constraints limit the relative pose of components and serve as a standard for judging assembly quality. However, in the traditional process of target pose estimation, a general method is needed for establishing the correlation between engineering constraints and product pose, and it is difficult to evaluate pose by constraints comprehensively. Therefore, the process of target pose estimation and evaluation is separated. In this paper, a pose coordination model based on multi-constraints is proposed, which includes pre-processing, pose estimation, pose adjustment and evaluation. Firstly, engineering constraints are decoupled into 4 types of Minimum Geometrical Reference Constraints (MGRC), and the inequalities for solving target pose are formulated. Then the Constraint Coordination Index (CCI) is defined as the optimization objective to solve the target pose. Finally, with CCI as the numerical index, the target pose is evaluated to illustrate the quality of assembly. Taking the simulation experiment of wing-fuselage jointing as an example, the external and internal parameters of model are analyzed, and the pose estimation based on multi-constraints reduces the CCI by 12%, compared with the point-set-registration method.     

非合作慢旋目标即时状态位姿确定

对失效卫星等非合作慢旋目标进行在轨服务，需要精确测量追踪航天器与目标之间的姿态信息。因此，如何在复杂的光照条件下快速、准确地对非合作慢旋目标进行即时状态位姿确定具有一定的挑战性。应用ORB-SLAM技术，首先定位关键帧，并估计姿态。然后用当前帧的特征点与地图点对应的特征点进行匹配。最后，将完成匹配的特征点通过重投影确定其在地图中的位置，如果出现跟踪丢失，则根据已有的地图点估计姿态。实验结果表明：在复杂的光照条件下，分别对以10(°)/s角速度运动和以3(°)/s角速度运动的非合作目标进行测量，当测量稳定后，平均角速度误差约为0.1(°)/s和0.02(°)/s，可以满足工程上空间非合作目标相对姿态测量的精度要求。     

基于结构光的空间大目标特征重构与位姿测量

针对因缺少空间非合作大目标的全局特征而难以实现相对位姿测量的问题,提出利用点状光源与单目光学相机组成点结构光视觉测量系统进行特征重构与位姿测量的方法。以非合作大目标上尺寸未知的局部矩形特征为测量对象,首先建立点结构光视觉测量系统相对位姿测量模型;接着利用相对约束关系给出特征重构方法并获得隐性特征点;然后利用特征点计算测量系统与非合作大目标之间的相对转移矩阵得到相对位置和姿态。通过数字仿真校验该方法的有效性并对测量误差因素进行分析,仿真结果表明该测量方法是有效的。     

基于三维地形匹配的月球软着陆导航方法研究

针对月球探测器定点着陆任务的需要,研究了基于三维地形匹配的导航方法。对三维地形匹配问题,将高程方差图局部极值点作为三维地形特征,在构建地形特征之间的相对位置与角度不变量的基础上,提出了基于投票的三维地形匹配策略。针对测量噪声统计特性的不确定性,将Sage\|Husa噪声估计算子与迭代卡尔曼滤波相结合,通过对测量噪声进行在线估计有效避免滤波精度下降甚至滤波发散的出现。数字仿真结果表明所研究的导航方法能够有效地实现探测器相对位置和姿态的精确估计。     

面向在轨服务的相对位姿单目视觉确定的凸松弛优化方法

针对非合作目标之间基于特征点的相对位姿单目视觉确定问题,考虑利用自然特征导致误差增大等因素,提出一种基于凸松弛理论和LMI算法的相对位姿求解迭代方法。该方法在基于逆投影线构建的优化模型基础上,首先利用松弛理论将姿态矩阵的单位正交非凸等式约束松弛为不等式凸约束,并证明了松弛后的优化问题与原问题等价,即松弛后的凸问题取得最值时,姿态矩阵满足原等式约束。进一步将松弛后的姿态矩阵不等式凸约束表示成线性矩阵不等式形式,进而利用内点法进行求解,并利用全局收敛性定理证明了该算法的全局收敛性。以在轨服务为背景,仿真试验表明,利用该算法相对位姿可在7次迭代达到收敛,与传统SVD算法相比,在噪声较大的情况下,该算法计算精度提高近一倍,能够快速收敛并具有较强的鲁棒性。     

整流罩分离过程中运动位姿测量技术研究

针对整流罩分离过程中的空间目标三维位姿跟踪测量,设计了双目视觉测量系统,完成整流罩关键点的空间三维坐标测量。基于各特征点的图像处理、识别及跟踪测量算法,结合极线约束匹配,实现目标点的精确立体匹配,可完成不同目标特征点的三维空间坐标解算,进而获取不同运动姿态时的速度、加速度等关键信息,为整流罩机械设计、动力学设计提供参考依据。     

Pose measurement and motion estimation of non-cooperative satellite based on spatial circle feature

Malfunctioned satellites have seriously threatened orbital safety, and the capture of these satellites is of great significance. The pose measurement and the motion estimation of the tumbling satellite is the premise of capture. In this paper, the docking ring of the satellite is identified, which is equivalent to a spatial circle. Combined with the nozzle feature, the pose duality of the spatial circle can be eliminated. And the measurement accuracy is improved by minimizing the reprojection error of the docking ring and the nozzle. Due to the symmetry of the docking ring, the measured pose has only five degrees of freedom, losing the degree of freedom of rotation around the normal vector. In the motion estimation algorithm, the observability of the tumbling motion is firstly analyzed, then an error-state Kalman filter with inertia ratio constraints is designed. To improve the convergence speed and stability of the filter, a rough estimation algorithm of filter initial value based on linear term extraction and particle swarm optimization is proposed. The effectiveness of the pose measurement and motion estimation method is verified by simulations.     

一种低数据传输的多机器人实时视觉SLAM

针对多机器人视觉SLAM在实际应用中带宽受限的问题,设计了一种低数据传输的多机器人实时视觉SLAM系统.系统中引入了NetVLAD神经网络模型,通过改进NetVLAD降低了多机器人回环检测的计算资源占用,提高了回环检测的实时性.提出了一种针对描述子缺失情况下的特征匹配算法,提高了回环检测与相对量测的鲁棒性,并提出了一种增量式多机器人位姿图共享和优化方法.最后,通过在KITTI数据集进行测试,验证了该SLAM系统能有效减少多机器人通信过程中的数据传输,具有与单机器人SLAM相当的定位精度和实时性.     

基于点云深度学习的对称结构空间目标相对位姿测量

针对对称结构空间目标相对位姿解算过程中点云误匹配带来的误差问题，提出一种基于点云深度学习的对称结构空间目标相对位姿测量方法。首先设计空间目标点云特征提取网络及关键点回归网络，将位姿测量问题转换为空间目标点云关键点回归问题，通过两个并行的回归网络分别输出空间目标平移向量和具有固定标签的目标点云三维边界框角点；其次利用具有连续稳定标签的角点求解目标姿态，可有效解决目标的对称结构导致的点云误配准问题；最后通过仿真数据集的实验表明，该方法相比于传统的点云配准方法有更高的准确率，能够精确求解具有对称结构的空间目标相对位姿。     

MC-LRF based pose measurement system for shipborne aircraft automatic landing

Due to the portability and anti-interference ability, vision-based shipborne aircraft automatic landing systems have attracted the attention of researchers. In this paper, a Monocular Camera and Laser Range Finder (MC-LRF)-based pose measurement system is designed for shipborne aircraft automatic landing. First, the system represents the target ship using a set of sparse landmarks, and a two-stage model is adopted to detect landmarks on the target ship. The rough 6D pose is measured by solving a Perspective-n-Point problem. Then, once the rough pose is measured, a region-based pose refinement is used to continuously track the 6D pose in the subsequent image sequences. To address the low accuracy of monocular pose measurement in the depth direction, the designed system adopts a laser range finder to obtain an accurate range value. The measured rough pose is iteratively optimized using the accurate range measurement. Experimental results on synthetic and real images show that the system achieves robust and precise pose measurement of the target ship during automatic landing. The measurement means error is within 0.4° in rotation, and 0.2% in translation, meeting the requirements for automatic fixed-wing aircraft landing.Received 5 July 2022; revised 19 August 2022; accepted 27 September 2022.