Towards Accurate Reconstruction of Racing Tracks Using an Autonomous Mobile Robot

In this article, a LiDAR and an IMU are used to reconstruct the motion of a mobile platform and the environment. Specifically, we analyse the curb reconstruction and the displacement using only LiDAR and IMU data. The goal is to test different algorithms to reconstruct the curbs in a racetrack estimating their dimensions and shape. An ablation study with the tested algorithms was performed to evaluate the best solution for an autonomous guided vehicle that follows a human being walking around a racetrack. Using the LiDAR data, the acquired points are used to reconstruct the curb using different techniques, such as RANdom SAmple and Consensus (RANSAC) to extract planes and lines once the data is properly filtered. Then, the frames acquired by the LiDAR are merged together by employing the Iterative Closest Point (ICP) algorithm to reconstruct the whole scenario in which the robot moves. Thanks to the embedded IMU of the Ouster® OS1-128 model, it is also possible to explore different techniques, like Zero Velocity Potential Update (ZUPT), and a tailored experimental approach to reconstruct motion and trajectory of the mobile platform.