RI: Collaborative Research: Bion-Inspired Navigation

已经有成功的研究建立度量表示的环境需要与运动规划的任何导航任务。这种度量地图需要大量的存储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整合的优秀导航能力，其技术途径可以概括为地点建模和地图创建。一个抽象的层次结构，引入了视觉建模的地方与层的特征地标，显着的地区，和对象。 一种新的图像相似性分数将用于跟踪以及循环关闭，并且对感知混叠具有鲁棒性。 对象是从显著地标的训练集中学习的，并且在最高抽象级别中，根据它们的对象内容和空间中对象的星座来标记位置。 拓扑图由标记有地点标签的节点组成，并与从两个地点之间的相对姿势获得的对相邻节点的动作相关联。学习的地图将发生在空间的所有可能的拓扑的地方集。与生物学家的合作将试图根据从动物的角度获得的视觉输入来交叉验证假设。

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.