RI: Collaborative Research: Bion-Inspired Navigation

已经成功地研究了建立环境的度量表示法以及任何导航任务所需的运动规划。这种公制地图需要过多的存储空间来记忆机器人的轨迹和所有地标位置。另一方面，动物基于视觉感知和简单的路径整合具有良好的导航能力，其技术方法可以概括为地点建模和地图创建。引入了抽象层次结构，用于对具有特征地标、显著区域和对象层的位置进行可视化建模。一种新的图像相似性分数将被用于跟踪和闭合环路，并且对感知混叠具有鲁棒性。从显著地标的外观训练集学习对象，并根据其对象内容和空间中对象的星座来标记在最高抽象级别中的位置。拓扑图是由标有位置标签的节点组成的，并与从两个位置之间的相对姿势获得的相邻节点的动作相关联。地图的学习将在位置集的所有可能拓扑的空间中进行。与生物学家的合作将试图基于从动物角度获得的视觉输入来交叉验证假说。

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.