This paper proposes LONER, the first real-time LiDAR SLAM algorithm that uses a neural implicit scene representation. Existing implicit mapping methods for LiDAR show promising results in large-scale reconstruction, but either require groundtruth poses or run slower than real-time. In contrast, LONER uses LiDAR data to train an MLP to estimate a dense map in real-time, while simultaneously estimating the trajectory of the sensor. To achieve real-time performance, this paper proposes a novel information-theoretic loss function that accounts for the fact that different regions of the map may be learned to varying degrees throughout online training. % different regions of the map having varying degrees of uncertainty during online operation. The proposed method is evaluated qualitatively and quantitatively on two open-source datasets. This evaluation illustrates that the proposed loss function converges faster and leads to more accurate geometry reconstruction than other loss functions used in depth-supervised neural implicit frameworks. Finally, this paper shows that LONER estimates trajectories competitively with state-of-the-art LiDAR SLAM methods, while also producing dense maps competitive with existing real-time implicit mapping methods that use groundtruth poses.

翻译：本文提出LONER，这是首个利用神经隐式场景表示的实时激光雷达SLAM算法。现有的激光雷达隐式映射方法在大规模重建中展现出良好效果，但要么需要真实位姿，要么运行速度低于实时。相比之下，LONER使用激光雷达数据训练多层感知机（MLP），在实时估计密集地图的同时同步估计传感器轨迹。为实现实时性能，本文提出一种新颖的信息论损失函数，该函数考虑了地图不同区域在在线训练过程中可能具有不同学习程度的事实。该方法在两个开源数据集上进行了定性和定量评估。评估结果表明，与深度监督神经隐式框架中使用的其他损失函数相比，所提出的损失函数收敛更快，且能生成更精确的几何重建。最后，本文证明LONER的轨迹估计性能与最先进的激光雷达SLAM方法相当，同时生成的密集地图与使用真实位姿的现有实时隐式映射方法相媲美。