Place recognition is a key module for long-term SLAM systems. Current LiDAR-based place recognition methods usually use representations of point clouds such as unordered points or range images. These methods achieve high recall rates of retrieval, but their performance may degrade in the case of view variation or scene changes. In this work, we explore the potential of a different representation in place recognition, i.e. bird's eye view (BEV) images. We observe that the structural contents of BEV images are less influenced by rotations and translations of point clouds. We validate that, without any delicate design, a simple VGGNet trained on BEV images achieves comparable performance with the state-of-the-art place recognition methods in scenes of slight viewpoint changes. For more robust place recognition, we design a rotation-invariant network called BEVPlace. We use group convolution to extract rotation-equivariant local features from the images and NetVLAD for global feature aggregation. In addition, we observe that the distance between BEV features is correlated with the geometry distance of point clouds. Based on the observation, we develop a method to estimate the position of the query cloud, extending the usage of place recognition. The experiments conducted on large-scale public datasets show that our method 1) achieves state-of-the-art performance in terms of recall rates, 2) is robust to view changes, 3) shows strong generalization ability, and 4) can estimate the positions of query point clouds. Source codes are publicly available at https://github.com/zjuluolun/BEVPlace.

翻译：地点识别是长期SLAM系统的关键模块。当前基于激光雷达的地点识别方法通常采用无序点云或距离图像等表示形式。这些方法虽能达到较高的检索召回率，但在视角变化或场景变化情况下性能可能下降。本研究探索了另一种表示形式——鸟瞰图在地点识别中的潜力。我们观察到鸟瞰图的结构内容受点云旋转和平移的影响较小。实验结果验证，无需任何精巧设计，在鸟瞰图上训练的简单VGGNet即可在轻微视角变化的场景中取得与现有最优地点识别方法相当的性能。为实现更鲁棒的地点识别，我们设计了名为BEVPlace的旋转不变网络：采用组卷积从图像中提取旋转等变的局部特征，并使用NetVLAD进行全局特征聚合。此外，我们观察到鸟瞰图特征之间的距离与点云的几何距离具有相关性。基于这一发现，我们开发了估计查询点云位置的方法，拓展了地点识别的应用场景。在大规模公开数据集上的实验表明，本方法：1）在召回率指标上达到最优性能；2）对视角变化具有鲁棒性；3）展现出强泛化能力；4）可估计查询点云的位置。源代码已公开于https://github.com/zjuluolun/BEVPlace。