Ground-SLAM: Ground Constrained LiDAR SLAM for Structured Multi-Floor Environments
This work addresses localization and mapping challenges for robots or autonomous systems in indoor multi-floor settings, representing an incremental improvement over existing SLAM methods.
The paper tackles pose drift in LiDAR SLAM for structured multi-floor environments by incorporating ground constraints into a pose graph optimization framework, achieving superior accuracy in experimental results.
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.