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: this https URL

基于弹性和紧凑的基于点的隐式神经地图表示的 PIN-SLAM 系统提出了一种用于构建全局一致地图的 SLAM 系统。通过对局部隐式有符号距离场的增量学习和给定当前局部地图的位姿估计之间的交替进行，我们的方法使用无对应关系的点到隐式模型配准来进行。我们的隐式地图基于稀疏可优化的神经点，当闭环时，它们具有与全局位姿调整相对应的弹性和可变形性。通过神经点特征进行闭环检测。大量实验证实，PIN-SLAM 对各种环境均具有鲁棒性，并且适用于 LiDAR 和 RGB-D 相机等不同的范围传感器。在保持更一致、更紧凑的隐式地图的同时，PIN-SLAM 实现了与最先进的 LiDAR 路程计或 SLAM 系统相媲美或更好的位姿估计精度，并且优于最近的神经隐式 SLAM 方法，可以重构为准确和完整的网格。最后，由于用于高效神经点索引的体素哈希和无需最近点关联的快速隐式地图配准，PIN-SLAM 可以在中等 GPU 上以传感器帧速率运行。