Domain Independent Vision-Based Navigation

领域独立的基于视觉的导航

• 批准号: 9711967
• 负责人: David Kriegman
• 金额: $ 23.68万
• 依托单位: Yale University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1997
• 资助国家: 美国
• 起止时间: 1997-09-01 至 1999-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9711967&HistoricalAwards=false
• 关键词: Domain; Independent; Vision; Based; Navigation

The goal of this research is to develop methods for robust, domain-independent vision-based navigation suitable for both structured and unstructured environments. Visual tracking is used to monitor a set of image features (markers), and vision- based control is used to to guide the robot's motion from the image trajectory of the markers while avoiding obstacles. An environment is represented as a graph (map) which may be constructed under human control (e.g. giving the system a tour) or autonomously as the system explores. As the robot moves during the process of mapping, markers are automatically selected from the video stream and tracked. Rather than using prestored models of landmarks, markers are selected based on image content using a suite of domain-independent operators. The selected markers will be visually distinctive, unique within the image, and stable under varying viewpoint and illumination. During passive map making, the robot is taken on a tour, and it instantiates a graph representing the paths that the robot can follow; recognition is used to annotate the map. During active mapping, the robot systematically explores the environment and incrementally constructs the graph representation. These algorithms will be tested empirically on two mobile platforms in both indoor and outdoor environments.

本研究的目标是开发出一种适用于结构化和非结构化环境的鲁棒的、独立于领域的基于视觉的导航方法。 视觉跟踪用于监视一组图像特征（标记），并且基于视觉的控制用于从标记的图像轨迹引导机器人的运动同时避开障碍物。 环境被表示为图形（地图），其可以在人类控制下（例如，给系统一个游览）或在系统探索时自主地构造。 当机器人在地图绘制过程中移动时，标记会自动从视频流中选择并跟踪。 而不是使用预先存储的地标模型，标记选择基于图像内容使用一套域独立的运营商。 所选标记在视觉上是独特的，在图像中是唯一的，并且在不同的视角和照明下是稳定的。 在被动地图制作过程中，机器人被带到一个巡回赛，它实例化一个代表机器人可以遵循的路径的图形;识别用于注释地图。 在主动地图绘制过程中，机器人系统地探索环境， 构建图形 表示. 这些算法将在室内和室外环境中的两个移动的平台上进行经验测试。