A Robust Strategy of Map Quality Assessment for Autonomous Driving based on LIDAR Road-Surface Reflectance

Abstract

Automatic map quality assessment is a very important process to bring the mapping modules into levels four and five of autonomous driving. In this paper, we propose a robust framework to check the map quality on behalf of human beings with indicating the possible ghost areas without using ground truth. The essence is to conduct the assessment process in the image domain instead of the point cloud plane. Therefore, the road is described by a set of nodes and each node represents a considerable road texture in Absolute Coordinate System using LIDAR reflectivity. This converts the vehicle trajectory into grayscale images with encoding stationary landmarks and road shapes. In addition, the global position errors are converted into relative position errors between the nodes and transformed into ghosting effects in the image domain. Accordingly, a mechanism to evaluate the map quality at the revisited areas is proposed based on sharpness, luminance and structure factors of the road surface. The framework has been tested in challenging environments including open-sky areas, the world’s second-longest tunnel and courses of dense trees and high buildings. The experimental results have verified the novelty and reliability of the proposed strategy to provide very trustful map quality assessment by relying on map images only. Moreover, the system is scalable to compare the maps and significantly indicates the outperformance in terms of accuracy and quality.