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，首个采用神经隐式场景表示的实时LiDAR SLAM算法。现有面向LiDAR的隐式映射方法在大规模重建方面展现出良好潜力，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LiDAR数据训练MLP实现实时稠密地图估计，同时完成传感器轨迹同步估算。为实现实时性能，本文提出一种新颖的信息论损失函数，该函数充分考虑了在线训练过程中地图不同区域可能呈现不同学习程度的特点。通过在两个开源数据集上的定性与定量评估表明：与基于深度监督的神经隐式框架中使用的其他损失函数相比，所提损失函数不仅收敛更快，还能重建出更精确的几何结构。最终实验证明，LONER在轨迹估计精度上可与当前最先进的LiDAR SLAM方法相媲美，同时能生成与现有使用真值位姿的实时隐式映射方法竞争力相当的稠密地图。