用沃罗诺伊单元改进激光雷达定位的全局描述符，提升距离计算稳定性。
Voronoi-based Second-order Descriptor with Whitened Metric in LiDAR Place Recognition
- 结合沃罗诺伊单元与二阶池化，构建新型描述符聚合机制。
- 在Oxford Robotcar和Wild-Places上实现精度提升，尤其在复杂场景下表现更优。
- 适合需要高鲁棒性激光雷达定位的自动驾驶与机器人导航应用。
池化层在激光雷达场景识别（LPR）中负责将局部描述符聚合为可度量的全局描述符，其中二阶池化能捕捉局部描述符间的高阶交互。然而，现有方法沿用传统实现与后归一化策略，导致描述符不适用于欧氏距离计算。基于将NetVLAD解释为二阶统计量的最新观点，我们提出将二阶池化与沃罗诺伊单元的归纳偏置相结合。新方法通过局部描述符生成二阶矩阵，并对全局描述符进行白化处理，隐式度量马哈拉诺比斯距离，同时保留沃罗诺伊单元的聚类特性，采用多种技术缓解学习过程中的数值不稳定性。实验在Oxford Robotcar和Wild-Places基准上验证了性能提升，并分析了白化算法的数值影响。
原文摘要 · Abstract (English)
The pooling layer plays a vital role in aggregating local descriptors into the metrizable global descriptor in the LiDAR Place Recognition (LPR). In particular, the second-order pooling is capable of capturing higher-order interactions among local descriptors. However, its existing methods in the LPR adhere to conventional implementations and post-normalization, and incur the descriptor unsuitable for Euclidean distancing. Based on the recent interpretation that associates NetVLAD with the second-order statistics, we propose to integrate second-order pooling with the inductive bias from Voronoi cells. Our novel pooling method aggregates local descriptors to form the second-order matrix and whitens the global descriptor to implicitly measure the Mahalanobis distance while conserving the cluster property from Voronoi cells, addressing its numerical instability during learning with diverse techniques. We demonstrate its performance gains through the experiments conducted on the Oxford Robotcar and Wild-Places benchmarks and analyze the numerical effect of the proposed whitening algorithm.
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