DATASET

RISEDB

Collection: RISE : Robust Indoor Localization in Complex Scenarios  

Description

A novel public dataset for developing and benchmarking indoor localization systems. We have selected and 3D mapped a set of representative indoor environments including a large office building, a conference room, a workshop, an exhibition area and a restaurant. to collect long sequences of spherical and stereo images, together with all the sensor readings coming from a consumer smartphone and locate them inside the map with centimetre accuracy. The dataset addresses many of the limitations of existing indoor localization datasets regarding the scale and diversity of the mapped buildings; the number of acquired sequences under varying conditions; the accuracy of the ground-truth trajectory; the availability of a detailed 3D model and the availability of different sensor types. It enables the benchmarking of existing and the development of new indoor localization approaches, in particular for deep learning based systems that require large amounts of labeled training data.

Contact

Email
Carlos.SANCHEZ-BELENGUER (at) ec.europa.eu

Contributors

How to cite

European Commission, Joint Research Centre (JRC) (2020): RISEDB. European Commission, Joint Research Centre (JRC) [Dataset] PID: http://data.europa.eu/89h/b4afbf64-17ab-4872-aaff-d5fc74ac953a

Data access

  • A novel public dataset for developing and benchmarking indoor localization systems. We have selected and 3D mapped a set of representative indoor environments including a large office building, a conference room, a workshop, an exhibition area and a restaurant. to collect long sequences of spherical and stereo images, together with all the sensor readings coming from a consumer smartphone and locate them inside the map with centimetre accuracy. The dataset addresses many of the limitations of existing indoor localization datasets regarding the scale and diversity of the mapped buildings; the number of acquired sequences under varying conditions; the accuracy of the ground-truth trajectory; the availability of a detailed 3D model and the availability of different sensor types. It enables the benchmarking of existing and the development of new indoor localization approaches, in particular for deep learning based systems that require large amounts of labeled training data.

Complimentary video to RISE-DB publication
URL 
RISEdb: a Novel Indoor Localization Dataset
URL 

Publications

Publication
RISEdb: a Novel Indoor Localization Dataset
Sanchez Belenguer, C., Wolfart, E., Casado Coscolla, A. and Sequeira, V., RISEdb: a Novel Indoor Localization Dataset, In: 25th International Conference on Pattern Recognition (ICPR) 2020, 10-15 January 2021, Milan, Italy, PROCEEDINGS - INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, 2021, ISSN 1051-4651 (online), p. 9514 - 9521, JRC121470.
  • INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS, UNITED STATES
Publication page 
  • Abstract

    In this paper we introduce a novel public dataset for developing and benchmarking indoor localization systems. We have selected and 3D mapped a set of representative indoor environments including a large office building, a conference room, a workshop, an exhibition area and a restaurant. Our acquisition pipeline is based on a portable LiDAR SLAM backpack to map the buildings and to accurately track the pose of the user as it moves freely inside them. We introduce the calibration procedures that enable us to acquire and geo-reference live data coming from different independent sensors rigidly attached to the backpack. This has allowed us to collect long sequences of spherical and stereo images, together with all the sensor readings coming from a consumer smartphone and locate them inside the map with centimetre accuracy. The dataset addresses many of the limitations of existing indoor localization datasets regarding the scale and diversity of the mapped buildings; the number of acquired sequences under varying conditions; the accuracy of the ground-truth trajectory; the availability of a detailed 3D model and the availability of different sensor types. It enables the benchmarking of existing and the development of new indoor localization approaches, in particular for deep learning based systems that require large amounts of labeled training data.

Additional information

Published by
European Commission, Joint Research Centre
Created date
2020-12-16
Modified date
2021-05-21
Issued date
2020-12-23
Data theme(s)
Science and technology
Update frequency
irregular
Identifier
http://data.europa.eu/89h/b4afbf64-17ab-4872-aaff-d5fc74ac953a
Popularity