CAREER: Markov Chain Monte Carlo Methods for Large Scale Correspondence Problems in Computer Vision and Robotics

该项目的目标是研究计算机视觉和机器人技术中大规模通信问题的可处理方法。对应是许多视觉和机器人应用中的核心问题，而提议的研究集中在三个方面：从空间和时间的数字图像中进行大规模3D重建，使用移动机器人进行同步定位和绘图，以及跟踪大量视觉上相似的物体，例如蚁丘中的蚂蚁或人群中的人。为了超越现有的最先进的方法，本提案旨在研究通过马尔可夫链蒙特卡罗（MCMC）采样的近似推理。MCMC为一个难以解决的问题提供了近似的解决方案，并且相对于其他方法具有许多吸引人的优点。此外，将MCMC应用于该问题所获得的实践见解可以与其他领域交叉授粉并产生新的理论研究。就更广泛的影响而言，该项目的综合研究和教育计划将有助于培养下一代研究人员，他们熟悉这些在统计力学中首次发现的新方法。此外，该项目还通过博物馆展览和与当地高中的互动进行了强有力的推广。从更长远的角度来看，拟议的研究将使计算机视觉和机器人技术的新颖和大规模应用有望对社会产生深远的影响。机器人即将在我们的生活中发挥更大的作用，例如，现在越来越受欢迎的消费机器人就证明了这一点。更直接的是，廉价数码摄影和视频的出现，正以指数级增长数字图像的数量，这些数字图像可以以新的和创造性的方式使用、分析和重新合成。通信问题是许多这些新用途的核心。

The goal of this project is to investigate tractable approaches to large-scale correspondence problems in computer vision and robotics. Correspondence is a central problem in many vision and robotics applications, and the proposed research centers on three of those: large-scale 3D reconstruction from digital imagery in space and time, simultaneous localization and mapping using mobile robots, and tracking large numbers of visually similar objects, such as ants in an ant-hill or people in a crowd. To eclipse existing state of the art methods, this proposal aims to investigate approximate inference through Markov chain Monte Carlo (MCMC) sampling. MCMC provides an approximate solution for an otherwise intractable problem, and has a number of attractive advantages with respect to other approaches. In addition, practical insights gained in applying MCMC to this problem can cross-pollinate other fields and spawn new theoretical investigations. In terms of broader impact, this project's integrated research and education plan will help produce a next generation of researchers, intimately familiar with these new methods first discovered in statistical mechanics. In addition, the project has a strong outreach component through museum exhibits and interaction with local high schools. Taking a longer view, the proposed research will enable novel and large-scaleapplications of computer vision and robotics that are expected to have far-reachingimplications for society. Robots are on the verge of playing a much larger role in our lives, as evidenced for example by the increasingly popular consumer robots now available. More immediately, the advent of cheap digital photography and video is exponentially increasing the volume of digital imagery that can be used, analyzed, and re-synthesized in new and creative ways. The correspondence problem lies at the heart of many of these novel uses.