Abstract

Place recognition technology is very important for autono-mous driving. To realize the large-scale recognition task of 3D point clouds, we propose a large-scale 3D point cloud place recognition framework based on graph neural networks, which combines local and global features. In extracting features, instance segmentation is performed on the large scene point clouds first, and then the GNN network trains each segmented instance to obtain local attribute features. We construct a graph model with each object as a node and the relationship between them as edges, then obtain the global topological structure features of the scene. In calculating similar scores, we calculate the similarity vector of the global and local feature through a similarity network and cosine similarity, respectively. Finally, we fuse the similarity vectors and calculate the final similarity score. This paper uses the SemanticKitti and nuScenes datasets to verify the proposed method. Compared with the state-of-the-art deep learning-based place recognition method, the proposed method achieves the best results in the SemanticKitti and nuScenes datasets.