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系统的核心组成部分。本文提出一种用于构建全局一致性地图的SLAM系统——PIN-SLAM，其基于弹性且紧凑的点式隐式神经地图表示。该系统以距离测量值为输入，通过交替执行局部隐式有符号距离场的增量学习与利用当前局部地图进行免对应点、点到隐式模型的位姿估计。所提出的隐式地图基于稀疏可优化神经点，具有内在弹性与可变形特性，可在闭环检测时随全局位姿调整而自适应变形。闭环检测同样采用神经点特征实现。大量实验验证表明，PIN-SLAM对多种环境具有鲁棒性，且适用于LiDAR与RGB-D相机等不同距离传感器。PIN-SLAM的位姿估计精度达到或优于现有最优LiDAR里程计/SLAM系统，同时超越最新神经隐式SLAM方法，并保持一致性更高、高度紧凑的隐式地图，可重建为精确完整的网格模型。最后，得益于采用体素哈希实现高效的神经点索引以及无需最近点关联的快速隐式地图配准，PIN-SLAM可在中等算力GPU上以传感器帧率运行。代码将开源在：https://github.com/PRBonn/PIN_SLAM。