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 laserinertial 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.

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This paper presents a real-time and low-cost 3D perception and reconstruction system which is suitable for autonomous navigation and large-scale environment reconstruction. The 3D mapping system is based on a rotating 2D planar laser scanner driven by a step motor, which is suitable for continuous mapping. However, for such a continuous mapping system, the challenge is that the range measurements are received at different times when the 3D LiDAR is moving, which will result in big distortion of the local 3D point cloud. As a result, the errors in motion estimation can cause misregistration of the resulting point cloud. In order to continuously estimate the trajectory of the sensor, we first extract feature points from the local point cloud and then estimate the transformation between current frame to local map to get the LiDAR odometry. After that, we use the estimated motion to remove the distortion of the local point cloud and then register the undistorted local point cloud to the global point cloud to get accurate global map. Finally, we propose a coarse-to-fine graph optimization method to minimize the global drift. The proposed 3D sensor system is advantageous due to its mechanical simplicity, mobility, low weight, low cost, and real-time estimation. To validate the performance of the proposed system, we carried out several experiments to verify its accuracy, robustness, and efficiency. The experimental results show that our system can accurately estimate the trajectory of the sensor and build a quality 3D point cloud map simultaneously.