A Robust LaserInertial Odometry and Mapping Method for LargeScale Highway Environments In this paper, we propose a novel laserinertial odometry and mapping method to achieve realtime, lowdrift and robust pose estimation in largescale highway environments. The proposed method is mainly composed of four sequential modules, namely scan preprocessing module, dynamic object detection module, laserinertial odometry module and laser mapping module. Scan preprocessing 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 xe2x80x9cFrametoModelxe2x80x99u0027 scan matching strategy is used to create a static global map. We compare the performance of our method with two stateoftheart methods, LOAM and SuMa, using KITTI dataset and real highway scene dataset. Experiment results show that our method performs better than the stateoftheart methods in real highway environments and achieves competitive accuracy on the KITTI dataset.

Robust High Accuracy VisualInertialLaser SLAM System In recent years, many excellent works on visualinertial SLAM and laserbased SLAM have been proposed. Although inertial measurement unit (IMU) significantly improve the motion estimate performance by reducing the impact of illumination variation or textureless region on visual tracking, tracking failures occur when in such an environment for a long time. Similarly, when in structureless environments, laser module will fail since lack of sufficient geometric features. Besides, motion estimation by moving lidar has the problem of distortion since range measurements are received continuously. To solve these problems, we propose a robust and highaccuracy visualinertiallaser SLAM system. The system starts with a visualinertial tightlycoupled method for motion estimation, followed by scan matching to further optimize the estimation and register point cloud on the map. Furthermore, we enable modules to be adjusted automatically and flexibly. That is, when one of these modules fails, the remaining modules will undertake the motiontracking task. For further improving the accuracy, loop closure and proximity detection are implemented to eliminate drift accumulation. When loop or proximity is detected, we perform six degreeoffreedom (6DOF) pose graph optimization to achieve the global consistency. The performance of our system is verified on public dataset, and the experimental results show that the proposed method achieves superior accuracy against other stateoftheart algorithms.