RI: Collaborative Research: Bion-Inspired Navigation

对于建立环境的度量表示以及任何导航任务的运动规划，已经有了成功的研究。这样的度量地图虽然需要大量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，动物具有基于视觉感知和简单路径整合的优秀导航能力。技术方法可以概括为地点建模和地图创建。引入了一种抽象层次结构，通过特征地标层、显著区域层和对象层对地点进行可视化建模。一种新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。对象是从显著地标的训练集中学习的，在最高抽象层次上，根据对象内容和空间中对象的星座来标记位置。拓扑图由带有位置标签的节点组成，并与从两个位置之间的相对位姿获得的相邻节点的动作相关联。地图的学习将发生在所有可能的位置集拓扑空间中。与生物学家的合作将尝试交叉验证基于从动物角度获得的视觉输入的假设。

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.