Semantic 3D Grid Maps for Autonomous Driving
arXiv:2211.01700 · doi:10.1109/ITSC55140.2022.9922537
Abstract
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.
Submitted, accepted and presented at the 25th IEEE International Conference on Intelligent Transportation Systems (IEEE ITSC 2022)