RI: Collaborative Research: Bio-inspired Navigation

RI：合作研究：仿生导航

• 批准号: 0713260
• 负责人: Kostas Daniilidis
• 金额: $ 22.5万
• 依托单位: University of Pennsylvania
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-01 至 2010-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0713260&HistoricalAwards=false
• 关键词: RI; Collaborative; Research; Bio; inspired

There has been successful research on establishing metric representations of the environment required together with motion planning for any navigation task. Such metric maps require though excessive amounts of storage to memorize the robots' trajectories and all landmark positions. On the other hand, animals have excellent navigation capabilities based on visual sensing and simple path integration.The technical approach can be summarized in the modeling of places and the map creation. An abstraction hierarchy is introduced for the visual modeling of places with the layers of feature landmarks, salient regions, and objects. A novel image similarity score will be used for tracking as well as loop closing and is robust to perceptual aliasing. Objects are learned from training sets of appearances of salient landmarks and in the highest abstraction level places are labeled depending on their object content and the constellation of objects in space. Topological maps are made of nodes labeled with place labels and associated with an action to neighboring nodes obtained from the relative pose between the two places. Learning of the maps will happen in the space of all possible topologies of place sets. A collaboration with biologists will try to cross-validate hypotheses based on visual inputs obtained from the animal's viewpoint.

关于建立对任何导航任务的运动计划所需的环境指标的度量表示，已经进行了成功的研究。这样的度量图需要过多的存储空间来记住机器人的轨迹和所有地标位置。 另一方面，动物具有基于视觉传感和简单路径集成的出色导航功能。技术方法可以总结在位置的建模和地图创建中。引入了一个抽象层次结构，以供具有特征地标，显着区域和对象层的位置的可视化建模。 新颖的图像相似性评分将用于跟踪和循环闭合，并且对感知混叠都是可靠的。 从显着地标的外观训练集中学到的对象，在最高的抽象级别位置，根据其对象内容和太空中对象的星座进行标记。 拓扑图由标记有位置标签的节点制成，并与从两个地方之间的相对姿势获得的相邻节点的作用相关联。学习地图将发生在所有可能的拓扑设置的空间中。与生物学家的合作将尝试根据从动物的观点获得的视觉投入来交叉验证假设。