GCLO: Ground Constrained LiDAR Odometry with Low-drifts for GPS-denied Indoor Environments

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Abstract
LiDAR is widely adopted in Simultaneous Localization And Mapping (SLAM) and High Definition (HD) map production. The accuracy of LiDAR Odometry (LO) is of great importance, especially in GPS-denied environments. However, we found typical LO results are prone to drift upwards along the vertical direction in underground parking lots, leading to poor mapping results. This paper proposes a Ground Constrained LO method named GCLO, which exploits planar grounds in these specific environments to compress the vertical pose drifts. GCLO is divided into three parts. First, a sensor-centric sliding map is maintained, and the point-to-plane ICP method is implemented to perform the scan-to-map registration. Then, at each key-frame, the sliding map is recorded as a local map. Ground points nearby are segmented and modeled as a planar landmark in the form of Closest Point (CP) parameterization. Finally, planar ground landmarks observed at different key-frames are associated. The ground landmark observation constraints are fused into the pose graph optimization framework to improve the LO performance. Experimental results in HIK and KITTI datasets demonstrate GCLO's superior performances in terms of accuracy in indoor multi-floor parking lots and flat outdoor sites. The limitation of GCLO in adaptability for other environments is also discussed.