  In this paper, we propose a highly accurate continuous-time trajectory
estimation framework dedicated to SLAM (Simultaneous Localization and Mapping)
applications, which enables fuse high-frequency and asynchronous sensor data
effectively. We apply the proposed framework in a 3D LiDAR-inertial system for
evaluations. The proposed method adopts a non-rigid registration method for
continuous-time trajectory estimation and simultaneously removing the motion
distortion in LiDAR scans. Additionally, we propose a two-state continuous-time
trajectory correction method to efficiently and efficiently tackle the
computationally-intractable global optimization problem when loop closure
happens. We examine the accuracy of the proposed approach on several publicly
available datasets and the data we collected. The experimental results indicate
that the proposed method outperforms the discrete-time methods regarding
accuracy especially when aggressive motion occurs. Furthermore, we open source
our code at \url{https://github.com/APRIL-ZJU/clins} to benefit research
community.
