Mesh-based 3D Textured Urban Mapping

TL;DR

This paper introduces a novel urban mapping framework that combines lidar and image data to produce a continuous, textured 3D mesh for improved visualization and navigation in autonomous driving.

Contribution

It presents a joint estimation method for 3D textured urban maps using lidar and images, enhancing existing point cloud-based approaches with a continuous mesh representation.

Findings

Outperforms two state-of-the-art urban mapping algorithms on KITTI dataset

Provides more accurate and visually detailed 3D urban maps

Refines map quality through photometric consistency with images

Abstract

In the era of autonomous driving, urban mapping represents a core step to let vehicles interact with the urban context. Successful mapping algorithms have been proposed in the last decade building the map leveraging on data from a single sensor. The focus of the system presented in this paper is twofold: the joint estimation of a 3D map from lidar data and images, based on a 3D mesh, and its texturing. Indeed, even if most surveying vehicles for mapping are endowed by cameras and lidar, existing mapping algorithms usually rely on either images or lidar data; moreover both image-based and lidar-based systems often represent the map as a point cloud, while a continuous textured mesh representation would be useful for visualization and navigation purposes. In the proposed framework, we join the accuracy of the 3D lidar data, and the dense information and appearance carried by the images, in estimating a visibility consistent map upon the lidar measurements, and refining it photometrically through the acquired images. We evaluate the proposed framework against the KITTI dataset and we show the performance improvement with respect to two state of the art urban mapping algorithms, and two widely used surface reconstruction algorithms in Computer Graphics.