Accurate and robust localization and mapping are essential components for most autonomous robots. In this paper, we propose a SLAM system for building globally consistent maps, called PIN-SLAM, that is based on an elastic and compact point-based implicit neural map representation. Taking range measurements as input, our approach alternates between incremental learning of the local implicit signed distance field and the pose estimation given the current local map using a correspondence-free, point-to-implicit model registration. Our implicit map is based on sparse optimizable neural points, which are inherently elastic and deformable with the global pose adjustment when closing a loop. Loops are also detected using the neural point features. Extensive experiments validate that PIN-SLAM is robust to various environments and versatile to different range sensors such as LiDAR and RGB-D cameras. PIN-SLAM achieves pose estimation accuracy better or on par with the state-of-the-art LiDAR odometry or SLAM systems and outperforms the recent neural implicit SLAM approaches while maintaining a more consistent, and highly compact implicit map that can be reconstructed as accurate and complete meshes. Finally, thanks to the voxel hashing for efficient neural points indexing and the fast implicit map-based registration without closest point association, PIN-SLAM can run at the sensor frame rate on a moderate GPU. Codes will be available at: https://github.com/PRBonn/PIN_SLAM.

翻译：精确鲁棒的定位与建图是大多数自主机器人的核心组件。本文提出一种用于构建全局一致地图的SLAM系统——PIN-SLAM，其基于弹性且紧凑的点隐式神经地图表示。该方法以距离测量数据为输入，通过交替执行局部隐式符号距离场的增量学习，以及基于当前局部地图采用免对应点-隐式模型配准的姿态估计，实现同步定位与建图。我们的隐式地图基于稀疏可优化神经点，这些神经点具有固有弹性，可在回环闭合时随全局姿态调整而形变。回环检测同样利用神经点特征实现。大量实验验证表明，PIN-SLAM对不同环境具有强鲁棒性，并兼容激光雷达与RGB-D相机等多种距离传感器。PIN-SLAM的姿态估计精度达到或优于当前最先进的激光雷达里程计或SLAM系统，且优于近期神经隐式SLAM方法，同时能保持更一致、高度紧凑的隐式地图，该地图可重建为精确完整的网格模型。最后，得益于体素哈希索引实现的高效神经点检索，以及无需最近点关联的快速隐式地图配准，PIN-SLAM可在中等性能GPU上达到传感器帧率运行。代码将在以下地址开源：https://github.com/PRBonn/PIN_SLAM。