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Semantic 3D Maps for Autonomous Driving

2022-11-03 10:52:39
Ajinkya Khoche, Maciej K Wozniak, Daniel Duberg, Patric Jensfelt
arXiv_RO
           
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.

Abstract (translated)

URL

https://arxiv.org/abs/2211.01700

PDF

https://arxiv.org/pdf/2211.01700.pdf

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