Place recognition is a key module for long-term SLAM systems. Current LiDAR-based place recognition methods are usually based on 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 code will be made 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或距离图像等点云表示形式。这些方法虽能实现较高的检索召回率，但在视角变化或场景改变时性能可能下降。本研究探索了另一种表示形式——鸟瞰图（BEV）在位置识别中的潜力。我们观察到BEV图像的结构内容受点云旋转和平移的影响较小。实验验证表明，无需复杂设计，仅使用在BEV图像上训练的简单VGGNet即可在轻微视角变化场景中达到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：采用组卷积提取图像的旋转等变局部特征，并使用NetVLAD进行全局特征聚合。此外，我们观察到BEV特征距离与点云几何距离存在相关性。基于此发现，我们开发了查询点云位置估计方法，拓展了地点识别的应用场景。在大型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）展现强泛化能力；4）可估计查询点云位置。源代码将在https://github.com/zjuluolun/BEVPlace 公开。