Accurate, robust, and real-time LiDAR-based odometry (LO) is imperative for many applications like robot navigation, globally consistent 3D scene map reconstruction, or safe motion-planning. Though LiDAR sensor is known for its precise range measurement, the non-uniform and uncertain point sampling density induce structural inconsistencies. Hence, existing supervised and unsupervised point set registration methods fail to establish one-to-one matching correspondences between LiDAR frames. We introduce a novel deep learning-based real-time (approx. 35-40ms per frame) LO method that jointly learns accurate frame-to-frame correspondences and model's predictive uncertainty (PU) as evidence to safe-guard LO predictions. In this work, we propose (i) partial optimal transportation of LiDAR feature descriptor for robust LO estimation, (ii) joint learning of predictive uncertainty while learning odometry over driving sequences, and (iii) demonstrate how PU can serve as evidence for necessary pose-graph optimization when LO network is either under or over confident. We evaluate our method on KITTI dataset and show competitive performance, even superior generalization ability over recent state-of-the-art approaches. Source codes are available.

翻译：准确、鲁棒且实时的激光雷达里程计对于机器人导航、全局一致的三维场景地图重建以及安全运动规划等诸多应用至关重要。尽管激光雷达传感器以其精确的距离测量能力著称，但非均匀且不确定的点采样密度会导致结构不一致性。因此，现有的有监督和无监督点集配准方法难以在激光雷达帧间建立一一对应的匹配关系。我们提出了一种基于深度学习的新型实时（每帧约35-40毫秒）激光雷达里程计方法，该方法能够联合学习精确的帧间对应关系和模型的预测不确定性，以此作为保障激光雷达里程计预测的证据。在这项工作中，我们提出了：(i) 用于鲁棒激光雷达里程计估计的激光雷达特征描述子的部分最优传输方法；(ii) 在驾驶序列上学习里程计的同时联合学习预测不确定性；(iii) 展示了当激光雷达里程计网络过于自信或信心不足时，预测不确定性如何作为必要位姿图优化的证据。我们在KITTI数据集上评估了该方法，并展示了与近期最先进方法相比具有竞争力的性能，甚至展现出更优越的泛化能力。源代码已公开。