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This paper proposes a pose-graph attentional graph neural network, calledP-GAT, which compares (key)nodes between sequential and non-sequentialsub-graphs for place recognition tasks as opposed to a common frame-to-frameretrieval problem formulation currently implemented in SOTA place recognitionmethods. P-GAT uses the maximum spatial and temporal information betweenneighbour cloud descriptors -- generated by an existing encoder -- utilisingthe concept of pose-graph SLAM. Leveraging intra- and inter-attention and graphneural network, P-GAT relates point clouds captured in nearby locations inEuclidean space and their embeddings in feature space. Experimental results onthe large-scale publically available datasets demonstrate the effectiveness ofour approach in scenes lacking distinct features and when training and testingenvironments have different distributions (domain adaptation). Further, anexhaustive comparison with the state-of-the-art shows improvements inperformance gains. Code is available athttps://github.com/csiro-robotics/P-GAT.

Pose-Graph Attentional Graph Neural Network for Lidar Place Recognition