Mesh-based 3D textured urban mapping
Mesh-based 3D textured urban mapping
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DOI：
10.1109/iros.2017.8206186
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发表时间：
2017-08
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通讯作者：
Andrea Romanoni;Daniele Fiorenti;M. Matteucci
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文献类型：
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作者：
Andrea Romanoni;Daniele Fiorenti;M. Matteucci
在自动驾驶时代，城市地图是让车辆与城市环境互动的核心步骤。在过去的十年中，已经提出了成功的映射算法，利用来自单个传感器的数据构建地图。本文提出的系统的重点是双重的：联合估计的三维地图从激光雷达数据和图像，基于三维网格，其纹理。事实上，即使大多数用于测绘的测量车辆都配备有相机和激光雷达，现有的测绘算法通常依赖于图像或激光雷达数据;此外，基于图像和基于激光雷达的系统通常将地图表示为点云，而连续纹理网格表示对于可视化和导航目的是有用的。在建议的框架中，我们加入了三维激光雷达数据的准确性，密集的信息和图像所携带的外观，在估计的能见度一致的地图上的激光雷达测量，并通过所获得的图像进行光度细化。我们评估所提出的框架对KITTI数据集，我们表现出的性能改进方面的两个国家的最先进的城市映射算法，和两个广泛使用的表面重建算法在计算机图形学。
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.