PIN-SLAM: LiDAR SLAM Using a Point-Based Implicit Neural Representation
  for Achieving Global Map Consistency
Yue Pan Xingguang Zhong Louis Wiesmann Thorbjörn Posewsky Jens Behley Cyrill Stachniss
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.