Ground Edge based LIDAR Localization without a Reflectivity Calibration for Autonomous Driving
This addresses localization challenges in autonomous vehicles by removing the infeasible calibration step for mass production.
The paper tackles the problem of LIDAR localization for autonomous driving by proposing an edge-based grid representation that eliminates the need for reflectivity calibration, achieving better performance than state-of-the-art methods without extra computational cost.
In this work we propose an alternative formulation to the problem of ground reflectivity grid based localization involving laser scanned data from multiple LIDARs mounted on autonomous vehicles. The driving idea of our localization formulation is an alternative edge reflectivity grid representation which is invariant to laser source, angle of incidence, range and robot surveying motion. Such property eliminates the need of the post-factory reflectivity calibration whose time requirements are infeasible in mass produced robots/vehicles. Our experiments demonstrate that we can achieve better performance than state of the art on ground reflectivity inference-map based localization at no additional computational burden.