LiDAR-based place recognition plays a crucial role in Simultaneous Localization and Mapping (SLAM) and LiDAR localization. Despite the emergence of various deep learning-based and hand-crafting-based methods, rotation-induced place recognition failure remains a critical challenge. Existing studies address this limitation through specific training strategies or network structures. However, the former does not produce satisfactory results, while the latter focuses mainly on the reduced problem of SO(2) rotation invariance. Methods targeting SO(3) rotation invariance suffer from limitations in discrimination capability. In this paper, we propose a new method that employs Vector Neurons Network (VNN) to achieve SO(3) rotation invariance. We first extract rotation-equivariant features from neighboring points and map low-dimensional features to a high-dimensional space through VNN. Afterwards, we calculate the Euclidean and Cosine distance in the rotation-equivariant feature space as rotation-invariant feature descriptors. Finally, we aggregate the features using GeM pooling to obtain global descriptors. To address the significant information loss when formulating rotation-invariant descriptors, we propose computing distances between features at different layers within the Euclidean space neighborhood. This greatly improves the discriminability of the point cloud descriptors while ensuring computational efficiency. Experimental results on public datasets show that our approach significantly outperforms other baseline methods implementing rotation invariance, while achieving comparable results with current state-of-the-art place recognition methods that do not consider rotation issues.

翻译：激光雷达地点识别在同步定位与地图构建（SLAM）和激光雷达定位中扮演着关键角色。尽管已有多种基于深度学习或手工设计的方法出现，但由旋转引起的地点识别失败仍然是一个关键挑战。现有研究通过特定的训练策略或网络结构来解决此问题，然而前者未能产生令人满意的结果，后者则主要关注简化的SO(2)旋转不变性问题。针对SO(3)旋转不变性的方法在判别能力上存在局限性。本文提出了一种新方法，利用向量神经元网络（VNN）实现SO(3)旋转不变性。我们首先从相邻点中提取旋转等变特征，并通过VNN将低维特征映射到高维空间；随后，在旋转等变特征空间中计算欧氏距离和余弦距离，作为旋转不变的特征描述子；最后，利用GeM池化聚合特征，得到全局描述子。为解决构建旋转不变描述子时信息丢失严重的问题，我们提出在欧氏空间邻域内计算不同层级特征之间的距离。这在保证计算效率的同时，显著提高了点云描述子的判别能力。在公开数据集上的实验结果表明，我们的方法在实现旋转不变性方面显著优于其他基线方法，且在未考虑旋转问题的情况下，与当前最先进的地点识别方法取得了可比的结果。