Imperial College London
Alternate

ProfessorAndrewDavison

Faculty of Engineering, Department of Computing

Professor of Robot Vision
 
 
 
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Contact

 

+44 (0)20 7594 8316a.davison Website

 
 
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Assistant

 

Ms Lucy Atthis +44 (0)20 7594 8259

 
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Location

 

303William Penney LaboratorySouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Canelhas:2017:10.3390/robotics6030015,
author = {Canelhas, DR and Schaffernicht, E and Stoyanov, T and Lilienthal, AJ and Davison, AJ},
doi = {10.3390/robotics6030015},
journal = {Robotics},
title = {Compressed voxel-based mapping using unsupervised learning},
url = {http://dx.doi.org/10.3390/robotics6030015},
volume = {6},
year = {2017}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - In order to deal with the scaling problem of volumetric map representations, we propose spatially local methods for high-ratio compression of 3D maps, represented as truncated signed distance fields. We show that these compressed maps can be used as meaningful descriptors for selective decompression in scenarios relevant to robotic applications. As compression methods, we compare using PCA-derived low-dimensional bases to nonlinear auto-encoder networks. Selecting two application-oriented performance metrics, we evaluate the impact of different compression rates on reconstruction fidelity as well as to the task of map-aided ego-motion estimation. It is demonstrated that lossily reconstructed distance fields used as cost functions for ego-motion estimation can outperform the original maps in challenging scenarios from standard RGB-D (color plus depth) data sets due to the rejection of high-frequency noise content.
AU  - Canelhas,DR
AU  - Schaffernicht,E
AU  - Stoyanov,T
AU  - Lilienthal,AJ
AU  - Davison,AJ
DO  - 10.3390/robotics6030015
PY  - 2017///
SN  - 2218-6581
TI  - Compressed voxel-based mapping using unsupervised learning
T2  - Robotics
UR  - http://dx.doi.org/10.3390/robotics6030015
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000419218300002&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - http://hdl.handle.net/10044/1/68662
VL  - 6
ER  -
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