Point cloud-based place recognition is crucial for mobile robots and autonomous vehicles, especially when the global positioning sensor is not accessible. LiDAR points are scattered on the surface of objects and buildings, which have strong shape priors along different axes. To enhance message passing along particular axes, Stacked Asymmetric Convolution Block (SACB) is designed, which is one of the main contributions in this paper. Comprehensive experiments demonstrate that asymmetric convolution and its corresponding strategies employed by SACB can contribute to the more effective representation of point cloud feature. On this basis, Selective Feature Fusion Block (SFFB), which is formed by stacking point- and channel-wise gating layers in a predefined sequence, is proposed to selectively boost salient local features in certain key regions, as well as to align the features before fusion phase. SACBs and SFFBs are combined to construct a robust and accurate architecture for point cloud-based place recognition, which is termed SelFLoc. Comparative experimental results show that SelFLoc achieves the state-of-the-art (SOTA) performance on the Oxford and other three in-house benchmarks with an improvement of 1.6 absolute percentages on mean average recall@1.

翻译：基于点云的地点识别对于移动机器人和自动驾驶车辆至关重要，特别是在无法使用全球定位传感器时。LiDAR点云分布在物体和建筑物表面，沿不同轴具有显著的形状先验。为强化沿特定轴的信息传递，本文设计了堆叠非对称卷积模块（SACB），这是本文的主要贡献之一。广泛实验表明，SACB所采用的非对称卷积及其相应策略有助于更有效地表征点云特征。在此基础上，本文提出选择性特征融合模块（SFFB），该模块通过按预设顺序堆叠逐点和逐通道门控层形成，旨在选择性增强关键区域中的显著局部特征，并在融合前对齐特征。SACB与SFFB结合构建了适用于点云地点识别的鲁棒且精确的架构，命名为SelFLoc。对比实验结果表明，SelFLoc在牛津数据集及其他三个内部基准上实现了最先进（SOTA）性能，在平均召回率@1指标上提升了1.6个绝对百分点。