This paper presents a complete topological navigation system for a resource-constrained mobile robot like Pepper, based on image memory and the teach-and-repeat paradigm. Image memory is constructed from a set of reference images that are acquired during a prior mapping phase and arranged topologically. A* search is used to find the optimal path between the current location and the destination. The images from the robot’s RGB camera are used to localize within the topological graph, and an Image-Based Visual Servoing (IBVS) control scheme drives the robot to the next node in the graph. Depth images update a local egocentric occupancy grid, and another IBVS controller navigates local free-space. The output of the two IBVS controllers is fused to form the final control command for the robot. We demonstrate real-time navigation for the Pepper robot in an indoor open-plan office environment without the need for accurate mapping and localization. Our core navigation module can run completely onboard the robot (which has quite limited computing capabilities) at 5 Hz without requiring any external computing resources. We have successfully performed navigation trials over 15 days, visiting more than 50 destinations and traveling more than 1200m with a success rate of over 80%. We discuss remaining challenges and openly share our software.

本文提出了一个完整的拓扑导航系统，用于像 Pepper 这样的资源受限的移动机器人，基于图像记忆和教导和重复范式。图像存储器由一组参考图像构成，这些参考图像是在先前的映射阶段获得并按拓扑排列。A*搜索用于寻找当前位置和目的地之间的最佳路径。来自机器人 RGB 摄像头的图像用于在拓扑图中定位，基于图像的视觉伺服 (IBVS) 控制方案将机器人驱动到图中的下一个节点。深度图像更新本地以自我为中心的占用网格，另一个 IBVS 控制器导航本地自由空间。两个 IBVS 控制器的输出融合形成机器人的最终控制命令。我们在室内开放式办公环境中演示了 Pepper 机器人的实时导航，而无需进行精确的映射和定位。我们的核心导航模块可以完全在机器人上运行（计算能力非常有限），频率为 5 Hz，无需任何外部计算资源。我们已经成功进行了15天的导航试验，访问了50多个目的地，航行了1200多米，成功率超过80%。我们讨论剩余的挑战并公开分享我们的软件。我们已经成功进行了15天的导航试验，访问了50多个目的地，航行了1200多米，成功率超过80%。我们讨论剩余的挑战并公开分享我们的软件。我们已经成功进行了15天的导航试验，访问了50多个目的地，航行了1200多米，成功率超过80%。我们讨论剩余的挑战并公开分享我们的软件。