Domain Independent Vision-Based Navigation

这项研究的目标是开发适用于结构化和非结构化环境的稳健的、与领域无关的基于视觉的导航方法。视觉跟踪用于监控一组图像特征(标志物)，基于视觉的控制用于引导机器人在避开障碍物的同时根据标志物的图像轨迹进行运动。环境被表示为图形(地图)，该图形(地图)可以在人工控制(例如，带系统参观)下构建，也可以在系统探索时自主构建。当机器人在映射过程中移动时，会自动从视频流中选择标记并进行跟踪。不是使用预先存储的地标模型，而是使用一套与域无关的运算符基于图像内容来选择标记。选定的标记将在视觉上与众不同，在图像中独一无二，并在不同的视点和照明下保持稳定。在被动地图制作过程中，机器人进行巡视，并实例化一个表示机器人可以遵循的路径的图形；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，并增量地构建图形表示。这些算法将在室内和室外环境下的两个移动平台上进行经验测试。

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.