Globally localizing a mobile robot in a known map is often a foundation for enabling robots to navigate and operate autonomously. In indoor environments, traditional Monte Carlo localization based on occupancy grid maps is considered the gold standard, but its accuracy is limited by the representation capabilities of the occupancy grid map. In this paper, we address the problem of building an effective map representation that allows to accurately perform probabilistic global localization. To this end, we propose an implicit neural map representation that is able to capture positional and directional geometric features from 2D LiDAR scans to efficiently represent the environment and learn a neural network that is able to predict both, the non-projective signed distance and a direction-aware projective distance for an arbitrary point in the mapped environment. This combination of neural map representation with a light-weight neural network allows us to design an efficient observation model within a conventional Monte Carlo localization framework for pose estimation of a robot in real time. We evaluated our approach to indoor localization on a publicly available dataset for global localization and the experimental results indicate that our approach is able to more accurately localize a mobile robot than other localization approaches employing occupancy or existing neural map representations. In contrast to other approaches employing an implicit neural map representation for 2D LiDAR localization, our approach allows to perform real-time pose tracking after convergence and near real-time global localization. The code of our approach is available at: https://github.com/PRBonn/enm-mcl.

翻译：在已知地图中实现移动机器人的全局定位，通常是使机器人能够自主导航与操作的基础。在室内环境中，基于占据栅格地图的传统蒙特卡洛定位被视为黄金标准，但其精度受限于占据栅格地图的表征能力。本文致力于构建一种有效的地图表征，以实现精确的概率全局定位。为此，我们提出一种隐式神经地图表示方法，该方法能够从二维激光雷达扫描中捕获位置与方向几何特征，从而高效表征环境，并训练一个神经网络，该网络能够预测映射环境中任意点的非投影符号距离和方向感知投影距离。这种神经地图表示与轻量级神经网络的结合，使我们能够在传统蒙特卡洛定位框架内设计一种高效的观测模型，以实时估计机器人位姿。我们在公开可用的全局定位数据集上评估了所提室内定位方法，实验结果表明，相较于采用占据栅格或现有神经地图表示的其他定位方法，我们的方法能够更精确地对移动机器人进行定位。与采用隐式神经地图表示进行二维激光雷达定位的其他方法相比，我们的方法能够在收敛后实现实时位姿跟踪和近实时全局定位。本方法的代码发布于：https://github.com/PRBonn/enm-mcl。