鲁棒的视觉示教与重复导航，采用灵活的拓扑度量图地图表示
Robust Visual Teach-and-Repeat Navigation with Flexible Topo-metric Graph Map Representation
- Department of Automation, University of Science and Technology of China (USTC)（自动化系，中国科学技术大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出一种视觉示教与重复导航系统，采用拓扑度量图地图表示、关键帧聚类匹配和长期目标管理，实现鲁棒轨迹重复导航，实验证明优于基线。
AI中文摘要：
视觉示教与重复导航是移动机器人在未知环境中部署的直接解决方案。然而，由于环境变化和动态物体的存在，鲁棒的轨迹重复导航仍然面临挑战。在本文中，我们提出了一种新颖的视觉示教与重复导航系统，该系统由灵活的地图表示、鲁棒的地图匹配和无地图的局部导航模块组成。在示教过程中，记录的关键帧被构建为拓扑度量图，每个节点可以进一步扩展以保存新的观测。这种表示还减轻了对全局一致建图的需求。为了在重复过程中增强地点识别性能，我们不再使用帧到帧匹配，而是首先实现关键帧聚类，将相似且连接的关键帧聚合成局部地图，并基于视觉帧到局部地图匹配策略进行地点识别。为了促进局部目标持续跟踪性能，构建了一个长期目标管理算法，该算法可以避免机器人因环境变化或障碍物遮挡而丢失目标。为了实现无地图的目标到达，提出了一种局部轨迹控制候选优化算法。我们在移动平台上进行了大量实验。结果表明，我们的系统在鲁棒性和有效性方面优于基线方法。
英文摘要：
Visual Teach-and-Repeat Navigation is a direct solution for mobile robot to be deployed in unknown environments. However, robust trajectory repeat navigation still remains challenged due to environmental changing and dynamic objects. In this paper, we propose a novel visual teach-and-repeat navigation system, which consists of a flexible map representation, robust map matching and a map-less local navigation module. During the teaching process, the recorded keyframes are formulated as a topo-metric graph and each node can be further extended to save new observations. Such representation also alleviates the requirement of globally consistent mapping. To enhance the place recognition performance during repeating process, instead of using frame-to-frame matching, we firstly implement keyframe clustering to aggregate similar connected keyframes into local map and perform place recognition based on visual frame-tolocal map matching strategy. To promote the local goal persistent tracking performance, a long-term goal management algorithm is constructed, which can avoid the robot getting lost due to environmental changes or obstacle occlusion. To achieve the goal without map, a local trajectory-control candidate optimization algorithm is proposed. Extensively experiments are conducted on our mobile platform. The results demonstrate that our system is superior to the baselines in terms of robustness and effectiveness.
