RI: Small: Micro-GPS: Localization using Visual Landmarks in Commonplace Texture

该项目开发了一种微型gps系统，可以提供厘米级的精度，并且在室内和室外都是可靠的，基于世界上“随机”纹理中的特定地标。关键的想法是，所有的地板，如建筑物中的地毯、木地板的纹理、人行道上的混凝土和道路上的沥青，在不同的位置都有小的缺陷、凸起或颜色的变化。安装在车辆下方的一个向下指向的摄像头可以观察到这些看似随机的变化的具体、独特的排列，并在索引中查找它们，以找到它们在世界上的精确位置。开发的技术可以提供更好的车内导航功能，例如在特定地点准确停车，避免坑洼和车道偏离警告。其他的应用可能包括可以在人行道上行走、避开粗糙路面的智能轮椅、为老年人和残疾人提供的滑板车、为视障人士提供的辅助技术、无标记的智能高速公路、仓库里可以精确定位在货架旁边的智能机器人，甚至是可以在家里处理日常家务的家庭助理。这项研究基于一个关键的想法，即定位是可能的，基于世界上存在的“随机”纹理的特定特征：看似异质的纹理，在任何地方都有独特的变化，但全球一致的图像统计。该项目的主要挑战包括开发以下方法：(1)在地面特写图像中检测不常见的位置或“特征”；(2)计算每个检测到的地标的特征描述符，使其不受方向和光照变化的影响；(3)将地物与地图进行匹配：预先建立的地物、地物排列及其在世界上的位置数据库；(4)能够创建和更新数据库以增加覆盖范围并解释变化。所有这些都是现代系统中用于跟踪、图像对齐和识别的常见组件。然而，单个算法已经被调整为最适合“自然”图像。相反，该项目专注于开发检测器、描述符、匹配算法和更新策略，以调整到普通地面纹理的统计数据。研究小组研究了将基于颜色的描述符与基于表面法线或高度场的描述符结合起来是否可以提高准确性；系统问题涉及到将系统扩展到广泛覆盖范围。

This project develops a Micro-GPS system that provides centimeter-level accuracy and is reliable both indoors and out, based on specific landmarks in the "random" textures present in the world. The key idea is that all floors, such as the carpet in a building, the grain of a wood floor, the concrete on a sidewalk, and the asphalt on a road, have small imperfections, bumps, or variations in color from location to location. A downward-pointing camera mounted underneath a vehicle can observe specific, unique arrangements of these seemingly random variations, looking them up in an index to find out their precise position in the world. The developed technology can provide capabilities for better in-car navigations, such as accurate parking in a particular spot, pothole avoidance, and lane departure warning. Other applications might include smart wheelchairs that can stay on a sidewalk and avoid rough patches, scooters for the elderly and disabled, assistive technologies for the visually impaired, marker-free smart highways, smart robots in warehouses that can precisely position themselves next to shelves, and even domestic assistants that can handle day-to-day chores inside a home.This research is based on a key idea that localization is possible based on specific features in the "random" textures present in the world: seemingly-heterogeneous textures that have unique variations everywhere but globally consistent image statistics. The key challenges of this project include developing methods for (1) detecting uncommon locations or "features" in a close-up image of the ground surface; (2) computing a feature descriptor for each detected landmark, in a way that is invariant to changes in orientation and lighting; (3) matching the features against a map: a pre-built database of features, their arrangements, and their locations in the world; and (4) being able to create and update the database to increase coverage and to account for changes. All of these are common components in contemporary systems for tracking, image alignment, and recognition. However, the individual algorithms have been tuned to work best for "natural" images. Instead, the project focuses on developing detectors, descriptors, matching algorithms, and update strategies that are tuned to the statistics of common ground textures. The research team investigates whether accuracy can be improved by combining descriptors based on color with ones based on surface normals or height fields; and the systems issues involved in scaling the system to widespread coverage.