  This paper proposes a 3D LiDAR SLAM algorithm named Ground-SLAM, which
exploits grounds in structured multi-floor environments to compress the pose
drift mainly caused by LiDAR measurement bias. Ground-SLAM is developed based
on the well-known pose graph optimization framework. In the front-end, motion
estimation is conducted using LiDAR Odometry (LO) with a novel sensor-centric
sliding map introduced, which is maintained by filtering out expired features
based on the model of error propagation. At each key-frame, the sliding map is
recorded as a local map. The ground nearby is extracted and modelled as an
infinite planar landmark in the form of Closest Point (CP) parameterization.
Then, ground planes observed at different key-frames are associated, and the
ground constraints are fused into the pose graph optimization framework to
compress the pose drift of LO. Finally, loop-closure detection is carried out,
and the residual error is jointly minimized, which could lead to a globally
consistent map. Experimental results demonstrate superior performances in the
accuracy of the proposed approach.
