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Dataset of the intermediate competition in challenge MALIN: Indoor–outdoor inertial navigation system data for pedestrian and vehicle with high accuracy references in a context of firefighter scenario

  • Zhu, Ni1
  • Ortiz, Miguel1
  • Renaudin, Valérie1
  • Ichard, Cécile2
  • Ricou, Sander2
  • 1 AME-GEOLOC, Université Gustave Eiffel, IFSTTAR, 44344 Bouguenais, France
  • 2 SGME, France
Published Article
Data in Brief
Publication Date
Dec 08, 2020
DOI: 10.1016/j.dib.2020.106626
PMID: 33354605
PMCID: PMC7744709
PubMed Central


This paper provides a multiple sensor dataset collected by the CyborgLOC team during the intermediate competition of the Challenge MALIN (MAîtrise de la localisation INdoor), which is a competition for indoor/outdoor real-time positioning. The sensors, including a GNSS receiver Ublox NEO-M8N, a Realsense D435i stereo camera, three Xsens MTi-300 and one PERSY (PEdestrian Reference SYstem), are mounted on different parts of the subject's body. The PERSY is a foot-mounted positioning device with a tri-axial accelerometer, a tri-axial gyroscope, a tri-axial magnetometer as well as a GNSS receiver Ublox M8T. The two scenarios are designed in a training centre of firefighters CFIS (Fire and Rescue Training Centre) in Blois, France to simulate the situation of firefighters during interventions. With total distances around 2 km for each scenario, the travelled trajectories passed through challenging environments including indoor, outdoor, urban canyon. The indoor part contains different stair levels, from the underground up to the 6th floor. The travel modes are vehicles and pedestrians. Several classical activities of firefighters are realized such as walking, running, stair-climbing, side-walking, crawling, passing above/below obstacles, carrying a stretcher, ladder climbing, etc. High accurate ground truth of stationary points and enclosing volumes are provided by the organizers of the competition, i.e., the Directorate General of Armaments (DGA: Direction Générale de l'Armement). Provided with raw data, they allow the evaluation of the positioning performances. This dataset is available on the data repository .

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