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3-D Sensor Algorithms for Spacecraft Pose Determination

机译：用于航天器姿态确定的3-D传感器算法

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Researchers at the Michigan Aerospace Corporation have developed accurate and robust 3-D algorithms for pose determination (position and orientation) of satellites as part of an on-going effort supporting autonomous rendezvous, docking and space situational awareness activities. 3-D range data from a LAser Detection And Ranging (LADAR) sensor is the expected input; however, the approach is unique in that the algorithms are designed to be sensor independent. Parameterized inputs allow the algorithms to be readily adapted to any sensor of opportunity. The cornerstone of our approach is the ability to simulate realistic range data that may be tailored to the specifications of any sensor. We were able to modify an open-source raytracing package to produce point cloud information from which high-fidelity simulated range images are generated. The assumptions made in our experimentation are as follows: 1) we have access to a CAD model of the target including information about the surface scattering and reflection characteristics of the components; 2) the satellite of interest may appear at any 3-D attitude; 3) the target is not necessarily rigid, but does have a limited number of configurations; and, 4) the target is not obscured in any way and is the only object in the field of view of the sensor. Our pose estimation approach then involves rendering a large number of exemplars (100k to 5M), extracting 2-D (silhouette- and projection-based) and 3-D (surface-based) features, and then training ensembles of decision trees to predict: a) the 4-D regions on a unit hypersphere into which the unit quaternion that represents the vehicle [Q_X, Q_Y, Q_Z, Q_W] is pointing, and, b) the components of that unit quaternion. Results have been quite promising and the tools and simulation environment developed for this application may also be applied to non-cooperative spacecraft operations, Autonomous Hazard Detection and Avoidance (AHDA) for landing craft, terrain mapping, vehicle guidance, path planning and obstacle avoidance.
机译：密歇根州航空航天公司的研究人员已经开发出了精确，可靠的3-D算法，用于确定卫星的姿态（位置和方向），这是正在进行的，支持自主会合，对接和空间态势感知活动的一项工作。来自激光检测和测距（LADAR）传感器的3D范围数据是预期的输入；但是，该方法的独特之处在于算法被设计为与传感器无关。参数化输入使算法可以轻松地适应任何机会传感器。我们方法的基石是能够模拟实际范围数据的能力，该范围数据可以针对任何传感器的规格进行定制。我们能够修改开源光线跟踪包，以生成点云信息，从该点云信息生成高保真模拟范围图像。我们在实验中做出的假设如下：1）我们可以访问目标的CAD模型，其中包括有关组件的表面散射和反射特性的信息； 2）感兴趣的卫星可能以任何3-D姿态出现； 3）目标不一定是刚性的，但确实具有有限数量的配置；并且，4）目标没有以任何方式被遮挡，并且是传感器视野中的唯一对象。然后，我们的姿势估计方法包括渲染大量示例（100k至5M），提取2-D（基于轮廓和投影的）和3-D（基于曲面）的特征，然后训练决策树的集合以进行预测：a）单位超球面上的代表车辆[Q_X，Q_Y，Q_Z，Q_W]的单位四元数指向的4-D区域，以及b）单位四元数的组成部分。结果是非常有希望的，为此应用程序开发的工具和仿真环境也可以应用于非合作航天器操作，用于着陆飞行器的自主危险检测和避免（AHDA），地形图，车辆导航，路径规划和避障。

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