  This paper presents a 3D lidar SLAM system based on improved regionalized
Gaussian process (GP) map reconstruction to provide both low-drift state
estimation and mapping in real-time for robotics applications. We utilize
spatial GP regression to model the environment. This tool enables us to recover
surfaces including those in sparsely scanned areas and obtain uniform samples
with uncertainty. Those properties facilitate robust data association and map
updating in our scan-to-map registration scheme, especially when working with
sparse range data. Compared with previous GP-SLAM, this work overcomes the
prohibitive computational complexity of GP and redesigns the registration
strategy to meet the accuracy requirements in 3D scenarios. For large-scale
tasks, a two-thread framework is employed to suppress the drift further. Aerial
and ground-based experiments demonstrate that our method allows robust odometry
and precise mapping in real-time. It also outperforms the state-of-the-art
lidar SLAM systems in our tests with light-weight sensors.
