Dataset for "ExMaps: Long-Term Localization in Dynamic Scenes using Exponential Decay"
This is the dataset that accompanies our publication "ExMaps: Long-Term Localization in Dynamic Scenes using Exponential Decay”. The data was collected over a period of time using a custom ARCore based android app. It depicts a retail aisle. The images can be found in the sub-folders “only_jpgs”. The rest of the ARCore data such as camera poses can be found in “data_all” subfolders for each day data was collected for. The data can be used to run the benchmarks from the original paper. It can also be used to reconstruct points clouds using SFM (structure from motion) software.
Cite this dataset as:
Rotsidis, A.,
2021.
Dataset for "ExMaps: Long-Term Localization in Dynamic Scenes using Exponential Decay".
Bath: University of Bath Research Data Archive.
Available from: https://doi.org/10.15125/BATH-00986.
Export
Data
exmaps_dataset.zip
application/zip (3GB)
Creative Commons: Attribution 4.0
Updated version 01/05/2021
Creators
Alexandros Rotsidis
University of Bath
Contributors
Christof Lutteroth
Supervisor
University of Bath
Peter Hall
Supervisor
University of Bath
Christian Richardt
Supervisor
University of Bath
University of Bath
Rights Holder
DcActiv
Sponsor
Documentation
Data collection method:
The data was collected over a number of weeks in a local grocery shop. It includes text files listing the 6DOF poses of the phone, and RGB frames. The frames and text files were acquired with a Google Pixel 2 phone, and the RGB frames were captured every 0.5 seconds at a resolution of 640 by 480.
Technical details and requirements:
A Google Pixel 2 phone was used for the collection of the data. The frames captures are the default camera frames that ARCore provides, under the name "CPU Images". The were stored locally on the phone and then extracted for use in our publication, "ExMaps: Long-Term Localization in Dynamic Scenes using Exponential Decay."
Additional information:
The data is provided in text files and RGB images, in a jpg format. The text files include additional information such as local poses.
Funders
Engineering and Physical Sciences Research Council (EPSRC)
https://doi.org/10.13039/501100000266
EPSRC Centre for Doctoral Training in Digital Entertainment
EP/L016540/1
Publication details
Publication date: 4 June 2021
by: University of Bath
Version: 1
DOI: https://doi.org/10.15125/BATH-00986
URL for this record: https://researchdata.bath.ac.uk/id/eprint/986
Related papers and books
Rotsidis, A., Lutteroth, C., Hall, P., and Richardt, C., 2021. ExMaps: Long-Term Localization in Dynamic Scenes using Exponential Decay. In: 2021 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE. Available from: https://doi.org/10.1109/wacv48630.2021.00291.
Related datasets and code
Rotsidis, A., 2021. Code for ExMaps: Long-Term Localization in Dynamic Scenes using Exponential Decay. GitHub. Available from: https://github.com/alexs7/ExMaps-Long-Term-Localization-in-Dynamic-Scenes-using-Exponential-Decay.
Contact information
Please contact the Research Data Service in the first instance for all matters concerning this item.
Contact person: Alexandros Rotsidis
Faculty of Science
Computer Science
Research Centres & Institutes
Centre for Digital Entertainment (CDE)
