  In this paper, we propose a novel laser-inertial odometry and mapping method
to achieve real-time, low-drift and robust pose estimation in large-scale
highway environments. The proposed method is mainly composed of four sequential
modules, namely scan pre-processing module, dynamic object detection module,
laser-inertial odometry module and laser mapping module. Scan pre-processing
module uses inertial measurements to compensate the motion distortion of each
laser scan. Then, the dynamic object detection module is used to detect and
remove dynamic objects from each laser scan by applying CNN segmentation
network. After obtaining the undistorted point cloud without moving objects,
the laser inertial odometry module uses an Error State Kalman Filter to fuse
the data of laser and IMU and output the coarse pose estimation at high
frequency. Finally, the laser mapping module performs a fine processing step
and the "Frame-to-Model" scan matching strategy is used to create a static
global map. We compare the performance of our method with two state-ofthe-art
methods, LOAM and SuMa, using KITTI dataset and real highway scene dataset.
Experiment results show that our method performs better than the
state-of-the-art methods in real highway environments and achieves competitive
accuracy on the KITTI dataset.
