Maps play a key role in rapidly developing area of autonomous driving. We survey the literature for different map representations and find that while the world is three-dimensional, it is common to rely on 2D map representations in order to meet real-time constraints. We believe that high levels of situation awareness require a 3D representation as well as the inclusion of semantic information. We demonstrate that our recently presented hierarchical 3D grid mapping framework UFOMap meets the real-time constraints. Furthermore, we show how it can be used to efficiently support more complex functions such as calculating the occluded parts of space and accumulating the output from a semantic segmentation network.
翻译:地图在迅速发展自主驾驶领域方面发挥着关键作用。我们为不同的地图图示调查文献,发现虽然世界是三维的,但通常依靠2D地图图示来应对实时限制。我们认为,高度的形势意识需要3D代表以及包含语义信息。我们证明,我们最近提出的三级3D电网绘图框架符合实时限制。此外,我们表明,如何利用它有效地支持更复杂的功能,如计算空间的隐密部分和积累语义分割网的产出。