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Article overview
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Pose-Graph Attentional Graph Neural Network for Lidar Place Recognition | | Milad Ramezani
; Liang Wang
; Joshua Knights
; Zhibin Li
; Pauline Pounds
; Peyman Moghadam
; | | Date: |
1 Sep 2023 | | Abstract: | This paper proposes a lidar place recognition approach, called P-GAT, to
increase the receptive field between point clouds captured over time. Instead
of comparing pairs of point clouds, we compare the similarity between sets of
point clouds to use the maximum spatial and temporal information between
neighbour clouds utilising the concept of pose-graph SLAM. Leveraging intra-
and inter-attention and graph neural network, P-GAT relates point clouds
captured in nearby locations in Euclidean space and their embeddings in feature
space. Experimental results on the large-scale publically available datasets
demonstrate the effectiveness of our approach in recognising scenes lacking
distinct features and when training and testing environments have different
distributions (domain adaptation). Further, an exhaustive comparison with the
state-of-the-art shows improvements in performance gains. Code will be
available upon acceptance. | | Source: | arXiv, 2309.00168 | | Services: | Forum | Review | PDF | Favorites | |
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