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，如激光雷达或立体视觉相机等距离传感器获得的数据，那么问题就在于表面配准，这是一个早期处理步骤，可以实现各种重要的新兴应用，如自动驾驶和同步定位和地图绘制，自动场景重建和物体建模，以及3D物体检索和识别。当存在数据集之间转换的初始估计时，问题就变成了一个细化问题，即局部注册。局部配准的有效解决方案已经有一段时间了，突出的是迭代最近点算法及其许多变体。最近，社区将注意力转向了更普遍和更困难的全球注册问题，其中没有对转换的初步估计。大多数全局配准方法遵循概率搜索，是启发式的。虽然最近提出了一种方法，但保证有效的解决方案仍然难以捉摸，特别是当数据集之间的重叠区域混乱或闭塞时，或者重叠程度很小。拟议的研究将建立并大大扩展我以前的工作到全球注册。我和我的学生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局注册问题。第二个短期目标将考虑将计算边界应用于潜在井空间嵌入中用于播种局部最小搜索过程的转换的影响。研究的长期目标是开发更有效的指标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支定界最优配准之间的桥梁。这些全球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在室外环境中一样改变室内导航。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周围环境，有效的全球注册将增强导航、识别和防撞能力。本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为加拿大工业在这一重要和令人兴奋的领域创造机会和就业机会。

Registration is the process of transforming two or more partially overlapping data sets to alignin the same coordinate reference frame. When the data are 3D point clouds, as acquired with rangesensors 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 3Dobject retrieval and recognition.When an initial estimate of the transformation between data sets exists, then the problem is oneof refinement, known as local registration. Effective solutions to local registration have been knownfor 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.