Automotive Radar For Localization In GNSS- Denied Environments

Abstract: Precise and robust automotive localization is a must for autonomous vehicles. Radar is a cheap and robust sensor, and this project aimed to find a method to use automotive radar to localize globally. By using radar data to build occupancy grids based on other state-of-the-art radar localization methods, and applying image correlation techniques, a localization precision of below 20 cm could be achieved, delivering poses at frequency higher than 0.5 Hz along with a characterization of the uncertainty. By using an improved sensor model for the occupancy grid mapping, filtering the radar data, and using image correlation in the Fourier domain. The presented results are better than the state-of-the-art radar localization methods, both in terms of precision and frequency, however not in terms of heading estimation. The work provides a foundation for future investigations and improvements of radar as a sensor for localization.