  Pose estimation purely based on 3D point-cloud could suffer from degradation,
e.g. scan blocks or scans in repetitive environments. To deal with this
problem, we propose an approach for fusing 3D spinning LiDAR and IMU to
estimate the ego-motion of the sensor body. The main idea of our work is to
optimize the poses and states of two kinds of sensors with non-linear
optimization methods. On the one hand, a bunch of IMU measurements are
considered as a relative constraint using pre-integration and the state errors
can be minimized with the help of laser pose estimation and non-linear
optimization algorithms; on the other hand, the optimized IMU pose outputs can
provide a better initial for the subsequent point-cloud matching. The method is
evaluated under both simulation and real tests with comparison to the
state-of-the-art. The results show that the proposed method can provide better
pose estimation performance even in the degradation cases.
