Microsoft indoor localization competition
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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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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

      • WiFi and sensors (accelerometer, gyro, etc.)

    • Infrastructure-based (13 teams)

      • Custom hardware deployment (magnetic, custom RF, light-based, etc.)


Microsoft indoor localization competition1

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


Microsoft indoor localization competition2

Microsoft Indoor Localization Competition


Microsoft indoor localization competition

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


Automated evaluation process

Automated Evaluation Process


Evaluation point impact

Evaluation point impact


Microsoft indoor localization competition

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


A huge thanks to

A huge THANKS to

Jie Liu

Filip Lemic

Jasper Buesch

Vlado Handziski


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