For many driving safety applications, it is of great importance to accurately register LiDAR point clouds generated on distant moving vehicles. However, such point clouds have extremely different point density and sensor perspective on the same object, making registration on such point clouds very hard. In this paper, we propose a novel feature extraction framework, called APR, for online distant point cloud registration. Specifically, APR leverages an autoencoder design, where the autoencoder reconstructs a denser aggregated point cloud with several frames instead of the original single input point cloud. Our design forces the encoder to extract features with rich local geometry information based on one single input point cloud. Such features are then used for online distant point cloud registration. We conduct extensive experiments against state-of-the-art (SOTA) feature extractors on KITTI and nuScenes datasets. Results show that APR outperforms all other extractors by a large margin, increasing average registration recall of SOTA extractors by 7.1% on LoKITTI and 4.6% on LoNuScenes. Code is available at https://github.com/liuQuan98/APR.

翻译：摘要：对于许多驾驶安全应用而言，准确配准来自远处运动车辆的激光雷达点云至关重要。然而，此类点云在同一物体上具有极其不同的点密度和传感器视角，这使得对此类点云的配准变得非常困难。本文提出了一种新颖的特征提取框架，称为APR，用于在线远距离点云配准。具体来说，APR采用自编码器设计，该自编码器利用若干帧重建一个更密集的聚合点云，而非原始的单一输入点云。我们的设计迫使编码器基于单一输入点云提取具有丰富局部几何信息的特征。这些特征随后被用于在线远距离点云配准。我们在KITTI和nuScenes数据集上进行了广泛的实验，与最先进的（SOTA）特征提取器进行了对比。结果表明，APR以较大优势优于所有其他提取器，在LoKITTI上平均配准召回率提升了7.1%，在LoNuScenes上提升了4.6%。代码已开源在https://github.com/liuQuan98/APR。