Efficient Robust Global Registration of 3D Data

配准是将两个或多个部分重叠的数据集转换为在同一坐标参考系中对齐*的过程。当数据是3D点云时，如用激光雷达或立体视觉摄像机等距离*传感器获取的，则问题是曲面配准，*这是支持大量重要新兴应用的早期处理步骤，例如自动驾驶和同步定位和映射、自动场景重建和对象建模以及3D*对象检索和识别。*当存在对数据集之间转换的初始估计时，问题是一次*精化，称为局部配准。局部配准的有效解决方案已经知道*一段时间了，突出的是迭代最近点算法及其许多变体。最近，社区将注意力转向更普遍和更困难的全球注册问题，在这个问题上，没有对转变的初步估计。全球注册的大多数方法都遵循概率搜索，并且是启发式的。虽然最近提出了一种办法，但既有保证又有效的解决办法仍然难以实现，特别是当数据集之间的重叠区域杂乱或遮挡，或重叠程度较小时。*拟议的研究将以我以前的工作为基础，并将其显著扩展到全球登记中。我的学生和我将在短期内扩展我们对虚拟兴趣点的调查，以解决非刚性转换下的全球注册问题。第二个短期目标将考虑将计算的边界应用于用于在潜在井空间嵌入中播种局部最小值搜索过程的变换的影响。这项研究的长期目标是开发更有效的衡量标准和方法来评估注册结果的质量。第二个长期目标是探索启发式次优配准和分枝定界最优配准之间的桥梁。*这些全局配准技术的进步有望实现一系列重要的应用，例如使用实时嵌入式距离传感器进行灵活的室内定位，它的出现将像GPS在室外环境中一样对室内导航产生变革。另一个相关的应用是自动驾驶汽车，它通过距离传感器持续监控环境，有效的全球注册将使导航、识别和避免碰撞的能力得到增强。这个研究项目的学生将获得计算机视觉一般领域的专业知识，特别是全球注册。他们的工作将促进这一领域的知识，他们获得的独特技能将创造机会和创造就业机会，使加拿大工业在这一重要和令人兴奋的领域受益。

Registration is the process of transforming two or more partially overlapping data sets to align***in the same coordinate reference frame. When the data are 3D point clouds, as acquired with range***sensors such as LiDAR or stereovision cameras, then the problem is one of surface registration,***which is an early processing step enabling a large variety of important emerging applications, such as autonomous driving and Simultaneous Localization and Mapping, automated scene reconstruction and object modelling, and 3D***object retrieval and recognition.******When an initial estimate of the transformation between data sets exists, then the problem is one***of refinement, known as local registration. Effective solutions to local registration have been known***for some time, prominently the Iterative Closest Point Algorithm and its many variants. More recently, the community has turned its attention to the more general and difficult problem of global registration, wherein no initial estimate of the transformation exists. Most approaches to global registration follow a probabilistic search and are heuristic. While an approach has recently been proposed, solutions that are both guaranteed and efficient remain elusive, especially when the region of overlap between the data sets is cluttered or occluded, or the degree of overlap is small.******The proposed research will build upon and significantly extend my previous work into global registration. My students and I will in the short-term extend our investigation of Virtual Interest Points to address global registration under non-rigid transformations. A second short-term goal will consider the impact of applying calculated boundaries to the transformations used to seed the local minima search process in Potential Well Space Embedding. A long term goal of the research is to develop more effective metrics and methods to evaluate the quality of a registration result. A second long term goal is to explore bridges between heuristic suboptimal and branch-and-bound optimal registration.******The advancement of these global registration techniques promise to enable an important set of applications, such as the use of realtime embedded range sensors for flexible indoor localization, the advent of which will be as transformative to indoor navigation as GPS has been in outdoor environments. Another related application is that of self-driving automobiles, which continually monitor their environments with range sensors, and for which effective global registration will enable enhanced navigation, recognition, and collision avoidance capabilities. The students in this research program will gain expertise in the general field of Computer Vision, specifically global registration. Their work will advance knowledge in this area, and the unique skills that they acquire will generate opportunities and job creation to the benefit of Canadian industry in this vital and exciting field.