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.

翻译：摘要：本文提出一种名为P-GAT的激光雷达场景识别方法，旨在扩大随时间采集的点云之间的感受野。不同于比较成对点云，我们利用位姿图SLAM的概念，通过比较点云集合间的相似性来最大化相邻点云间的时空信息。借助内部注意力、交叉注意力及图神经网络，P-GAT将欧氏空间中邻近位置采集的点云与其特征空间中的嵌入相关联。在公开大规模数据集上的实验结果证明了该方法在识别缺乏显著特征的场景，以及训练与测试环境存在分布差异（领域自适应）时的有效性。此外，与最先进方法的全面对比显示了性能增益的提升。代码将在论文接收后公开。