标题： 基于生物启发的混合地图：移动协作机器人导航的空间隐式局部框架和拓扑地图 显示英文标题

标题： Bio-Inspired Hybrid Map: Spatial Implicit Local Frames and Topological Map for Mobile Cobot Navigation

摘要： 导航是移动机器人的一项基本能力，使它们能够在复杂和动态的环境中自主运行。 传统方法使用概率模型，利用传感器观测同时对机器人进行定位和地图构建。 近期的方法采用人类启发式学习，如模仿学习和强化学习，以更有效地导航机器人。 然而，这些方法存在计算成本高、全局地图不一致以及对未见过环境泛化能力差的问题。 本文提出了一种受人类如何在新环境中有效感知和导航启发的新方法。 具体来说，我们首先构建局部框架，模拟人类如何在短期内表示关键的空间信息。 局部框架中的点是混合表示，包括空间信息和学习到的特征，称为空间隐式局部框架。 然后，我们将空间隐式局部框架整合到以因子图表示的全局拓扑地图中。 最后，我们开发了一种基于快速探索随机树星（RRT*）的新导航算法，该算法利用空间隐式局部框架和拓扑地图在环境中有效导航。 为了验证我们的方法，我们在真实世界数据集和实验室环境中进行了大量实验。 我们在 https://github.com/tuantdang/simn 开源我们的源代码。 显示英文摘要

摘要： Navigation is a fundamental capacity for mobile robots, enabling them to operate autonomously in complex and dynamic environments. Conventional approaches use probabilistic models to localize robots and build maps simultaneously using sensor observations. Recent approaches employ human-inspired learning, such as imitation and reinforcement learning, to navigate robots more effectively. However, these methods suffer from high computational costs, global map inconsistency, and poor generalization to unseen environments. This paper presents a novel method inspired by how humans perceive and navigate themselves effectively in novel environments. Specifically, we first build local frames that mimic how humans represent essential spatial information in the short term. Points in local frames are hybrid representations, including spatial information and learned features, so-called spatial-implicit local frames. Then, we integrate spatial-implicit local frames into the global topological map represented as a factor graph. Lastly, we developed a novel navigation algorithm based on Rapid-Exploring Random Tree Star (RRT*) that leverages spatial-implicit local frames and the topological map to navigate effectively in environments. To validate our approach, we conduct extensive experiments in real-world datasets and in-lab environments. We open our source code at https://github.com/tuantdang/simn}{https://github.com/tuantdang/simn.