Ground-Edge-Based LIDAR Localization Without a Reflectivity Calibration for Autonomous Driving

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 a property eliminates the need of the postfactory 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 inferencemap-based localization at no additional computational burden.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 a property eliminates the need of the postfactory 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.