Your smartphone goes everywhere with you—whether driving to the grocery store or shopping for holiday gifts. With your permission, apps can use your location to provide contextual information. You might get driving directions, find a store, or receive alerts for nearby promotions. These handy features are enabled by GPS, which requires outdoor exposure for the best accuracy. Yet, there are many times when you’re inside large structures, such as a shopping mall or event center. Accurate indoor positioning, based on public sensors and user permission, allows for a great location-based experience even when you aren’t outside.
Current positioning solutions have poor accuracy, particularly in multi-level buildings, or generalize poorly to small datasets. Additionally, GPS was built for a time before smartphones. Today’s use cases often require more granularity than is typically available indoors.
In this competition, your task is to predict the indoor position of smartphones based on real-time sensor data, provided by indoor positioning technology company XYZ10 in partnership with Microsoft Research. You’ll locate devices using “active” localization data, which is made available with the cooperation of the user. Unlike passive localization methods (e.g. radar, camera), the data provided for this competition requires explicit user permission. You’ll work with a dataset of nearly 30,000 traces from over 200 buildings.
If successful, you’ll contribute to research with broad-reaching possibilities, including industries like manufacturing, retail, and autonomous devices. With more accurate positioning, existing location-based apps could even be improved. Perhaps you’ll even see the benefits yourself the next time you hit the mall.
Awards:-
- 1st Place – $5,000
- 2nd Place – $3,000
- 3rd Place – $2,000
Note that, per the competition rules, there is no requirement for winners to license their solutions. However the host team asks that in lieu of solution-licensing, winners must attend one of their virtual workshop/panel events to present their work, share their learnings and join the discussion with other practitioners. Such attendance is as a condition to earn Prizes.
Deadline:- 10-05-2021
