  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.
