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Microsoft Indoor Localization Competition

Microsoft Indoor Localization Competition. 36 submissions from 32 teams 21 teams with 22 systems eventually participated Academia, Industry, Startups Submissions were classified into two categories Infrastructure-free (9 teams) No hardware deployment

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Microsoft Indoor Localization Competition

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  1. Microsoft Indoor Localization Competition • 36 submissions from 32 teams • 21 teams with 22 systems eventually participated • Academia, Industry, Startups • Submissions were classified into two categories • Infrastructure-free (9 teams) • No hardware deployment • WiFi and sensors (accelerometer, gyro, etc.) • Infrastructure-based (13 teams) • Custom hardware deployment (magnetic, custom RF, light-based, etc.)

  2. Microsoft Indoor Localization Competition • 2-day event • Day 1: Sunday • Teams were given 7 hours to setup and calibrate their systems • Day 2: Monday • Each team was asked to provide the coordinates of 20 test points • Test points’ coordinates were manually measured using laser range finders • Evaluation Metric • Average localization error across the 20 test points

  3. Microsoft Indoor Localization Competition

  4. Infrastructure Based Infrastructure Free EasyPoint Arne Bestmann, Ronne Reimann (Lamda:4Entwicklungen GmbH) MapUme Martin Klepal and Christian Beder (Cork University of Technology) 1st 1.6m error 0.7m error $1000 Visible Light Localization L. Li, C. Zhao, J. Shen, F. Zhao (Microsoft Research) Accurate Multi-Sensor Localization on Android C. Laoudias, G. Larkou, C. Li, Y.-K. Tsai, D. Zeinalipour-Yazti, C. G. Panayiotou (University of Cyprus) 2nd 2m error 2m error $500 WiFi-based Indoor Localization H. Zou, H. Jiang, L. Xie (Nanyang Technological University) FUBLoc S. Adler, S. Schmitt, Y. Yang, Y. Zhao, M. Kyas (FreieUniversitat Berlin) 3rd 2.2m error 2m error $300 An Indoor Location Solution for Mobile Devices A. S. Ferraz, A. G. Alvino, L. Q. L. Martins, P. A. Bello (Ubee S.A.) ALPS: An Ultrasonic Localization System P. Lazik, N. Rajagopal, B. Sinopoli, A. Rowe (Carnegie Mellon University) 4th 2.8m error 2.1m error $200

  5. Automated Evaluation Process

  6. Evaluation point impact

  7. Hallway Room containing the origin point Furniture remained the same between setup and evaluation Room without the origin point Furniture was rearranged between setup and evaluation

  8. A huge THANKS to Jie Liu Filip Lemic Jasper Buesch Vlado Handziski

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