Towards autonomous driving in a parking garage: Vehicle localization and tracking using environment-embedded LIDAR sensors

In this paper, we propose a new approach for localization and tracking of a vehicle in a parking garage, based on environment-embedded LIDAR sensors. In particular, we present an integration of data from multiple sensors, allowing to track vehicles in a common, parking garage coordinate system. In order to perform detection and tracking in realtime, a combination of appropriate methods, namely a grid-based approach, a RANSAC algorithm, and a Kalman filter is proposed and evaluated. The system achieves highly confident and exact vehicle positioning. In the context of a larger framework, our approach was used as a reference system to enable autonomous driving within a parking garage. In our experiments, we showed that the proposed algorithm allows a precise vehicle localization and tracking. Our system's results were compared to human-labeled ground-truth data. Based on this comparison we prove a high accuracy with a mean lateral and longitudinal error of 6.3cm and 8.5 cm, respectively.