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Comparing View-Based and Map-Based Semantic Labelling in Real-Time SLAM

2020-02-24 16:12:51
Zoe Landgraf, Fabian Falck, Michael Bloesch, Stefan Leutenegger, Andrew Davison
arXiv_CV
     
Abstract

Generally capable Spatial AI systems must build persistent scene representations where geometric models are combined with meaningful semantic labels. The many approaches to labelling scenes can be divided into two clear groups: view-based which estimate labels from the input view-wise data and then incrementally fuse them into the scene model as it is built; and map-based which label the generated scene model. However, there has so far been no attempt to quantitatively compare view-based and map-based labelling. Here, we present an experimental framework and comparison which uses real-time height map fusion as an accessible platform for a fair comparison, opening up the route to further systematic research in this area.

Abstract (translated)

URL

https://arxiv.org/abs/2002.10342

PDF

https://arxiv.org/pdf/2002.10342.pdf

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