Domain Independent Vision-Based Navigation

本研究的目标是开发适合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。视觉跟踪用于监控一组图像特征（标记），基于视觉的控制用于引导机器人从标记的图像轨迹中运动，同时避开障碍物。一个环境被表示为一个图形（地图），它可以在人类的控制下构建（例如给系统一个游览），也可以在系统探索时自主构建。当机器人在绘图过程中移动时，会自动从视频流中选择标记并进行跟踪。它不是使用预先存储的地标模型，而是使用一套领域无关的操作符根据图像内容选择标记。所选择的标记将在视觉上与众不同，在图像中是唯一的，并且在不同的视点和照明下是稳定的。在被动地图制作过程中，机器人被带着进行一次旅行，它实例化了一个表示机器人可以遵循的路径的图；识别用于对地图进行注释。在主动映射过程中，机器人系统地探索环境并逐步构建图形表示。这些算法将在室内和室外环境的两个移动平台上进行实证测试。

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.