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Compressed Voxel-Based Mapping Using Unsupervised Learning
Örebro University, School of Science and Technology. (Center for Autonomous Applied Sensor Systems (AASS))ORCID iD: 0000-0001-7035-5710
Örebro University, School of Science and Technology. (Center for Autonomous Applied Sensor Systems (AASS))ORCID iD: 0000-0003-4026-7490
Örebro University, School of Science and Technology. (Center for Autonomous Applied Sensor Systems (AASS))ORCID iD: 0000-0002-6013-4874
Örebro University, School of Science and Technology. (Center for Autonomous Applied Sensor Systems (AASS))ORCID iD: 0000-0003-0217-9326
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2017 (English)In: Robotics, E-ISSN 2218-6581, Vol. 6, no 3, article id 15Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Basel, Switzerland: MDPI AG , 2017. Vol. 6, no 3, article id 15
Keywords [en]
3D mapping, TSDF, compression, dictionary learning, auto-encoder, denoising
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:oru:diva-64420DOI: 10.3390/robotics6030015ISI: 000419218300002Scopus ID: 2-s2.0-85030989493OAI: oai:DiVA.org:oru-64420DiVA, id: diva2:1175909
Note

Funding Agencies:

European Commission  FP7-ICT-270350 

H-ICT  732737 

Available from: 2018-01-19 Created: 2018-01-19 Last updated: 2018-01-19Bibliographically approved

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Canelhas, Daniel R.Schaffernicht, ErikStoyanov, TodorLilienthal, Achim

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