Automated laser tracking evaluation and scan constraint analysis for pose estimation (ALTESCAPE)

拟议项目将开发新的方法，使用主动激光雷达传感器对航天器和类似物体进行智能实时跟踪。近几十年来，主动传感器已成为传统CCD相机的替代品，避免了广泛的地面支持和飞行前计划的需要。瑞尔森大学、加拿大航天局和Neptec DesignGroup的研究人员之间的合作将解决目前阻碍LIDAR系统的几个操作和算法挑战。该项目下开发的新技术将在Neptec的TriDAR跟踪系统上进行测试。这些创新将改善系统性能，提高可靠性，扩大TriDAR工业应用范围。自发光有源传感器不受可变的环境照明条件的影响，它用激光束扫描物体，并提供用于姿态估计的3D范围数据。主动传感器对于航天器之间的近距离操作特别有用。这类传感器可以测量相对位置、方向和速度，该项目的主要目标是：目标1 -代数约束分析的应用，以（a）帮助任务规划者选择有效的扫描区域和模式，（B）提供一致的跟踪精度实时评估;以及目标2 -基于卡尔曼滤波（KF）和其他信号处理技术的运动适应姿态估计的开发，以在（a）可能涉及快速运动和（B）静态跟踪精度时传播姿态，目标1中提到的资源有限。拟议的项目将有助于HQP培训，并将增加加拿大高等教育机构对加拿大空间方案感兴趣的先进科学和技术研究领域的参与。发达的基础设施将吸引来自大多伦多地区其他大学的研究，从而有可能获得真实的范围数据，以验证计算机视觉算法的性能。

The proposed project will develop novel approaches to intelligent real-time tracking of spacecraft and similarobjects using active LIDAR sensors. In recent decades, active sensors have become an alternative toconventional CCD cameras, avoiding the need for extensive ground support and pre-flight planning. Ourcollaboration between researchers at Ryerson University, the Canadian Space Agency, and Neptec DesignGroup will address several operational and algorithmic challenges that currently hamper LIDAR systems. Newtechnologies developed under this program will be tested on Neptec's TriDAR tracking system. Theseinnovations will improve system performance, increase reliability and expand the range of TriDAR industrialapplications. Immune to variable ambient lighting conditions, self-illuminating active sensors scan an objectwith a laser beam and deliver 3D range data for pose estimation. Active sensors are particularly useful forclose-range operations between spacecraft. Sensors of this type can measure relative position, orientation andspeed.The key goals of the project are: Goal 1 - the application of algebraic constraint analysis to (a) aid missionplanners in the selection of productive scanning area and patterns, and (b) provide a consistent real-timeassessment of tracking accuracy; and Goal 2 - the development of motion-accommodating pose estimationbased on Kalman Filtering (KF) and other signal processing techniques to propagate pose when (a) fast motionmay be involved, and (b) static tracking accuracy, as referred to in Goal 1, is limited. The proposed project willcontribute to HQP training and will increase the involvement of Canadian post-secondary institutions inadvanced science and technology research areas of interest to the Canadian Space Program. The developedinfrastructure will attract researches from other universities across the Great Toronto Area giving possibility toobtain real range data to validate the performance computer vision algorithms.