Topological maps are favorable for their small storage compared to geometric map. However, they are limited in relocalization and path planning capabilities. To solve this problem, a feature-based hierarchical topological map (FHT-Map) is proposed along with a real-time map construction algorithm for robot exploration. Specifically, the FHT-Map utilizes both RGB cameras and LiDAR information and consists of two types of nodes: main node and support node. Main nodes will store visual information compressed by convolutional neural network and local laser scan data to enhance subsequent relocalization capability. Support nodes retain a minimal amount of data to ensure storage efficiency while facilitating path planning. After map construction with robot exploration, the FHT-Map can be used by other robots for relocalization and path planning. Experiments are conducted in Gazebo simulator, and the results demonstrate that the proposed FHT-Map can effectively improve relocalization and path planning capability compared with other topological maps. Moreover, experiments on hierarchical architecture are implemented to show the necessity of two types of nodes.

翻译：拓扑地图相较于几何地图具有存储量小的优势，但在重定位和路径规划能力上存在局限。为解决该问题，提出了一种基于特征的层次拓扑地图（FHT-Map）及其用于机器人探索的实时地图构建算法。具体而言，FHT-Map同时利用RGB相机与激光雷达信息，包含两类节点：主节点与支撑节点。主节点存储经卷积神经网络压缩的视觉信息及局部激光扫描数据，以增强后续重定位能力；支撑节点保留最少数据量以确保存储效率，同时辅助路径规划。通过机器人探索完成地图构建后，FHT-Map可供其他机器人进行重定位与路径规划。在Gazebo仿真器中的实验表明，与现有拓扑地图相比，所提出的FHT-Map能有效提升重定位与路径规划性能。此外，针对层次架构的实验验证了两类节点存在的必要性。