Imperial College London
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DrStefanLeutenegger

Faculty of Engineering, Department of Computing

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s.leutenegger Website

 
 
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ACE ExtensionSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{McCormac:2017:10.1109/ICRA.2017.7989538,
author = {McCormac, J and Handa, A and Davison, AJ and Leutenegger, S},
doi = {10.1109/ICRA.2017.7989538},
pages = {4628--4635},
publisher = {IEEE},
title = {SemanticFusion: dense 3D semantic mapping with convolutional neural networks},
url = {http://dx.doi.org/10.1109/ICRA.2017.7989538},
year = {2017}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - Ever more robust, accurate and detailed mapping using visual sensing has proven to be an enabling factor for mobile robots across a wide variety of applications. For the next level of robot intelligence and intuitive user interaction, maps need to extend beyond geometry and appearance — they need to contain semantics. We address this challenge by combining Convolutional Neural Networks (CNNs) and a state-of-the-art dense Simultaneous Localization and Mapping (SLAM) system, ElasticFusion, which provides long-term dense correspondences between frames of indoor RGB-D video even during loopy scanning trajectories. These correspondences allow the CNN's semantic predictions from multiple view points to be probabilistically fused into a map. This not only produces a useful semantic 3D map, but we also show on the NYUv2 dataset that fusing multiple predictions leads to an improvement even in the 2D semantic labelling over baseline single frame predictions. We also show that for a smaller reconstruction dataset with larger variation in prediction viewpoint, the improvement over single frame segmentation increases. Our system is efficient enough to allow real-time interactive use at frame-rates of ≈25Hz.
AU  - McCormac,J
AU  - Handa,A
AU  - Davison,AJ
AU  - Leutenegger,S
DO  - 10.1109/ICRA.2017.7989538
EP  - 4635
PB  - IEEE
PY  - 2017///
SP  - 4628
TI  - SemanticFusion: dense 3D semantic mapping with convolutional neural networks
UR  - http://dx.doi.org/10.1109/ICRA.2017.7989538
UR  - http://hdl.handle.net/10044/1/49082
ER  -
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