FHT-Map: Feature-Based Hybrid Topological Map for Relocalization and Path Planning

Abstract

Topological maps are favorable for their small storage compared to geometric maps. However, they are limited in relocalization and path planning capabilities. To solve the problem, a feature-based hybrid topological map (FHT-Map) is proposed along with a real-time map construction algorithm based on 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 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 through robot exploration, the FHT-Map can be used by other robots for relocalization and path planning. Simulation results demonstrate that the proposed FHT-Map can effectively improve relocalization and path planning capability compared with other topological maps. Moreover, simulations of the hybrid architecture are implemented to show the necessity of two types of nodes. The FHT-Map is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/KunSong-L/FHT-Map</uri> .