期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2020
卷号：V-1-2020
页码：125-132
DOI：10.5194/isprs-annals-V-1-2020-125-2020
语种：English
出版社：Copernicus Publications
摘要：Environmental perception is one of the core requirements in autonomous vehicle navigation. If exposed to harsh conditions, commonly deployed sensors like cameras or lidars deliver poor sensing performance. Millimeter wave radars enable robust sensing of the environment, but suffer from specular reflections and large beamwidths. To incorporate the sensor noise and lateral uncertainty, a new probabilistic, voxel-based recursive mapping method is presented to enable online terrain mapping using scanning radar sensors. For map accuracy evaluation, test measurements are performed with a scanning radar sensor in an off-road area. The voxel map is used to derive a digital terrain model, which can be compared with ground-truth data from an image-based photogrammetric reconstruction of the terrain. The method evaluation shows promising results for terrain mapping solely performed with radar scanners. However, small terrain structures still pose a problem due to larger beamwidths in comparison to lidar sensors.
