Offline mapping for autonomous vehicles with low-cost sensors

Abstract

This paper presents an offline mapping algorithm for autonomous vehicles (AV) with low-cost sensors. The mapping algorithm consists of five key steps. First, data pre-processing 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.