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. 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：摘要：本文提出LONER——首个利用神经隐式场景表示的实时激光雷达SLAM算法。现有基于隐式映射的激光雷达方法在大规模重建中展现出良好效果，但要么需要真值位姿，要么运行速度低于实时要求。相比之下，LONER通过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运动轨迹。为达到实时性能，本文提出一种新型信息论损失函数，该函数考虑了在线训练过程中地图不同区域学习程度存在差异的特性。该方法在两个开源数据集上进行了定性与定量评估。实验结果表明：与深度监督神经隐式框架中使用的其他损失函数相比，所提损失函数收敛更快且能重建更精确的几何结构。最后，本文证明LONER在轨迹估计方面可与最先进的激光雷达SLAM方法相媲美，同时生成的密集地图也能与使用真值位姿的现有实时隐式映射方法竞争。