Having good knowledge of terrain information is essential for improving the performance of various downstream tasks on complex terrains, especially for the locomotion and navigation of legged robots. We present a novel framework for neural urban terrain reconstruction with uncertainty estimations. It generates dense robot-centric elevation maps online from sparse LiDAR observations. We design a novel pre-processing and point features representation approach that ensures high robustness and computational efficiency when integrating multiple point cloud frames. A Bayesian-GAN model then recovers the detailed terrain structures while simultaneously providing the pixel-wise reconstruction uncertainty. We evaluate the proposed pipeline through extensive simulation and real-world experiments. It demonstrates efficient terrain reconstruction with high quality and real-time performance on a mobile platform, which further benefits the downstream tasks of legged robots. (See https://kin-zhang.github.io/ndem/ for more details.)

翻译：对地形信息的充分了解对于提升复杂地形下各项下游任务（尤其是足式机器人的运动与导航）的性能至关重要。本文提出一种新颖的神经城市地形重建框架，该框架具备不确定性估计能力，能够从稀疏的激光雷达观测中在线生成以机器人为中心的稠密高程地图。我们设计了一种新颖的预处理与点特征表示方法，确保在整合多帧点云数据时具有高鲁棒性与计算效率。随后，采用贝叶斯生成对抗网络模型恢复详细的地形结构，同时提供逐像素的重建不确定性。通过大量仿真与真实世界实验对所提出的流程进行评估，结果表明该方法在移动平台上能够实现高质量、高效率的地形重建与实时性能，从而进一步惠及足式机器人的下游任务。（更多详情请参见 https://kin-zhang.github.io/ndem/）