Powered by OpenAIRE graph
Found an issue? Give us feedback
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2018
License: CC BY
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2018
License: CC BY
Data sources: Datacite
versions View all 2 versions
addClaim

Datasets and Supporting Materials for the IPIN 2018 Competition Track 3 (Smartphone-based, off-site)

Authors: Ruiz, Antonio Ramón Jiménez; Mendoza-Silva, Germán Martín; Ortiz, Miguel; Perez-Navarro, Antoni; Perul, Johan; Seco, Fernando; Torres-Sospedra, Joaquín;

Datasets and Supporting Materials for the IPIN 2018 Competition Track 3 (Smartphone-based, off-site)

Abstract

This package contains the datasets and supplementary materials used in the IPIN 2018 Competition (Nantes, France). Contents: IPIN2018_CallForCompetition_v2.1: Call for competition including the technical annex describing the competition 01-Logfiles: This folder contains a subfolder with the 22 training logfiles, a subfolder with the 15 (13 + 2) validation logfiles, and a subfolder with the 1 blind evaluation logfile as provided to competitors. 02-Supplementary_Materials: This folder contains the Matlab/octave parser, the raster maps, the vector maps and the visualization of the training routes. 03-Evaluation: This folder contains the scripts used to calculate the competition metric, the 75th percentile on the 99 evaluation points. The ground truth is also provided in MatLab format and as a CSV file. Since the results must be provided with a 2Hz freq. starting from apptimestamp 0, the GT includes the closest timestamp matching the timing provided by competitors. 03-Evaluation_alternative: This folder contains the alternative scripts used to calculate the competition metric, the 75th percentile on the 99 evaluation points. This version is compatible with MatLab and Octave and does not require any toolbox. In some cases, the differences in the reported errors might be around 10 cm with respect to the script used in the competition. The ground truth is also provided in MatLab format and as a CSV file. Since the results must be provided with a 2Hz freq. starting from apptimestamp 0, the GT includes the closest timestamp matching the timing provided by competitors. Please, cite the following works when using the datasets included in this package: Jimenez, A.R.; Mendoza-Silva, G.M.; Ortiz, M.; Perez-Navarro, A.; Perul, J.; Seco, F.; Torres-Sospedra, J. Datasets and Supporting Materials for the IPIN 2018 Competition Track 3 (Smartphone-based, off-site). http://dx.doi.org/10.5281/zenodo.2823964 Renaudin, V.; Ortiz, M.; Perul, J.; Torres-Sospedra, J.; Ramón Jimenez, A.; Pérez-Navarro, A.; Martín Mendoza-Silva, G.; Seco, F.; Landau, Y.; Marbel, R.; Ben-Moshe, B.; Zheng, X.; Ye, F.; Kuang, J.; Li, Y.; Niu, X.; Landa, V.; Hacohen, S.; Shvalb, N.; Lu, C.; Uchiyama, H.; Thomas, D.; Shimada, A.; Taniguchi, R.; Ding, Z.; Xu, F.; Kronenwett, N.; Vladimirov, B.; Lee, S.; Cho, E.; Jun, S.; Lee, C.; Park, S.; Lee, Y.; Rew, J.; Park, C.; Jeong, H.; Han, J.; Lee, K.; Zhang, W.; Li, X.; Wei, D.; Zhang, Y.; Park, S. Y.; Park, C. G.; Knauth, S.; Pipelidis, G.; Tsiamitros, N.; Lungenstrass, T.; Pablo Morales, J.; Trogh, J.; Plets, D.; Opiela, M.; Shih-Hau Fang Tsao, Y.; Chien, Y.-R.; Yang, S.-S.; Ye, S.-J.; Ali, M. U.; Hur, S.; and Park, Y. Evaluating Indoor Positioning Systems in a Shopping Mall: The Lessons Learned from the IPIN 2018 Competition IEEE Access Vol. 7, pp. 148594-148628, 2019. http://dx.doi.org/10.1109/ACCESS.2019.2944389 Additional information can be found at: http://evaal.aaloa.org/2018/call-for-competitions http://ipin-conference.org/2018/ipincompetition/ For any further questions about the database and this competition track, please contact: Joaquín Torres (jtorres@uji.es) Institute of New Imaging Technologies, Universitat Jaume I, Spain. Antonio R. Jiménez (antonio.jimenez@csic.es) Center of Automation and Robotics (CAR)-CSIC/UPM, Spain.

We would like to thank Atlantis le Centre for sponsoring the competition track with an award for the winning team and Viametris for producing the ground truth with their the mobile mapping technology. We are also grateful to Francesco Potortì, Sangjoon Park and the GEOLOC team for their invaluable help in organizing and promoting the IPIN competition and conference. Parts of this work were carried out with the financial support received from projects and grants: REPNIN+ network (TEC2017-90808-REDT), LORIS (TIN2012-38080-C04-04), TARSIUS (TIN2015-71564-C4-2-R, MINECO/FEDER), SmartLoc(CSIC-PIE Ref.201450E011), GEO-C (Project ID: 642332, H2020-MSCA-ITN-2014, Marie Sklodowska-Curie Action: Innovative Training Networks).

Keywords

RF Fingerprinting, Sensor Fusion, Indoor Positioning, Particle filter, Kalman filter, Pedestrian Dead Reckoning, Competition datasets, Indoor Navigation, Map Matching

  • BIP!
    Impact byBIP!
    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    0
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
    OpenAIRE UsageCounts
    Usage byUsageCounts
    visibility views 41
    download downloads 13
  • 41
    views
    13
    downloads
    Powered byOpenAIRE UsageCounts
Powered by OpenAIRE graph
Found an issue? Give us feedback
visibility
download
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
OpenAIRE UsageCountsViews provided by UsageCounts
downloads
OpenAIRE UsageCountsDownloads provided by UsageCounts
0
Average
Average
Average
41
13
Funded by