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.

在建立任何导航任务所需的环境的度量表示以及运动规划方面已经取得了成功的研究。这种度量地图需要大量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，动物具有基于视觉感知和简单路径集成的出色导航能力。其技术途径可以概括为场所建模和地图创建。引入了抽象层次结构，用于具有特征地标、显着区域和对象层的地点的视觉建模。 一种新颖的图像相似度评分将用于跟踪和闭环，并且对感知混叠具有鲁棒性。 对象是从显着地标的外观训练集中学习的，并且在最高抽象级别的位置根据其对象内容和空间中对象的星座进行标记。 拓扑图由标有地点标签的节点组成，并与从两个地点之间的相对姿态获得的相邻节点的动作相关联。地图的学习将发生在地点集所有可能的拓扑空间中。与生物学家的合作将尝试根据从动物的角度获得的视觉输入来交叉验证假设。