Offline mapping for autonomous vehicles with lowcost sensors Abstract   This paper presents an offline mapping algorithm for autonomous vehicles (AV) with lowcost sensors. The mapping algorithm consists of five key steps. First, data preprocessing is conducted to calibrate the original odometry data. Then, based on a 2D laser scanner and the calibrated odometry data, a virtual 3D light detection and ranging (LiDAR) is built. In the third step, loop closure is performed to search the revisited region and calculate the distance displacement. Afterward, the optimizer is applied to generate the final trajectory. Finally, by fusing the point cloud data from virtual 3D LiDAR and the final trajectory, the point cloud map is generated. Field experiments are conducted in both open and urban areas. In these two cases, satisfactory point cloud maps are built even the vehicle travels a long distance with loop closure, and the constructed point cloud map can be used for AV localization.

Sparse3D Lidar Outdoor MapBased Autonomous Vehicle Localization Difficulties in capturing unique structures in the outdoor environment hinders the mapbased Autonomous Vehicles (AV) localization performance. Accordingly, this necessitates the use of high resolution sensors to capture more information from the environment. However, this approach is costly and limits the mass deployment of AV. To overcome this drawback, in this paper, we propose a novel outdoor mapbased localization method for Autonomous Vehicles in urban environments using sparse 3D lidar scan data. In the proposed method, a PointtoDistribution (P2D) formulation of the Normal Distributions Transform (NDT) approach is applied in a Monte Carlo Localization (MCL) framework. The formulation improves the measurement model of localization by taking individual lidar point measurements into consideration. Additionally, to apply the localization to scalable outdoor environments, a flexible and efficient map structure is implemented. The experimental results indicate that the proposed approach significantly improves the localization and its robustness in outdoor AV environments, especially with limited sparse lidar data.