Ground Edge based LIDAR Localization without a Reflectivity Calibration for Autonomous Driving

TL;DR

This paper introduces a novel ground edge-based LIDAR localization method that does not require reflectivity calibration, improving efficiency and performance in autonomous vehicle localization.

Contribution

It presents an invariant edge reflectivity grid representation that eliminates the need for reflectivity calibration, simplifying the localization process.

Findings

Achieves better localization performance than state-of-the-art methods.

Eliminates the need for time-consuming reflectivity calibration.

Operates without additional computational burden.

Abstract

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.