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.

该研究提出了一种激光雷达场景识别方法，称为 P-GAT，通过比较一组点云之间的相似性来增加随时间捕获的点云之间的可接受领域，利用基于位姿图 SLAM 的概念，结合了点云之间的内部和外部注意力以及图神经网络来关联在欧几里德空间中附近位置捕获的点云以及其在特征空间中的嵌入。实验证明了该方法在识别缺乏明显特征的场景以及训练和测试环境具有不同分布（领域适应）时的有效性。与最先进技术的详尽比较显示出性能改进。代码将在接受后提供。