Prospect for Indoor Localization Kaifei Chen UC Berkeley
Prospect for Indoor Localization Kaifei Chen UC Berkeley
Why indoor localization is important Applications Indoor Parking Lots Localization Services Shopping Mall Other Services Building Operating System (BOSS) Conference Rooms 3/2/2021 Indoor Emergency Rescue Software-Defined Building 2
Existing Indoor Localization Systems Fingerprint-Based Localization 3/2/2021 Beacon-Based Localization Motion Sensor-Based Localization (Dead Reckoning) Software-Defined Building 3
Why Still Work On Indoor Localization • They have assumptions and limitations – Fingerprints : ubiquitous, unique, and consistent – Beacons : extra infrastructures, overhead – Dead reckoning : normal movement of phones • Disconnection between services and applications – Over-emphasis on physical accuracy 3/2/2021 Software-Defined Building 4
Why Still Work On Indoor Localization Shopping Mall Indoor Parking Lots Conference Rooms 3/2/2021 Indoor Emergency Rescue Software-Defined Building 5
Why Still Work On Indoor Localization • They have assumptions and limitations – Fingerprints : ubiquitous, unique, and consistent – Beacons : extra infrastructures – Dead reckoning : normal movement of phones • Disconnection between services and applications – Over-emphasis on physical accuracy – Applications mostly need semantic information 3/2/2021 Software-Defined Building 6
Why Still Work On Indoor Localization Shopping Mall Indoor Parking Lots Conference Rooms 3/2/2021 Indoor Emergency Rescue Software-Defined Building 7
Why Still Work On Indoor Localization • They have assumptions and limitations – Fingerprints : ubiquitous, unique, and consistent – Beacons : extra infrastructures – Dead reckoning : normal movement of phones • Disconnection between services and applications – Over-emphasis on physical accuracy – Applications mostly need semantic information • Too much administrator and user efforts 3/2/2021 Software-Defined Building 8
Our Solutions • Combine Indoor Localization Systems – Limitations are mutually independent – Context awareness and localization confidence – Take advantages of data fusion techniques • Semantic Localization • Automatic Data Collection and Map Generation 3/2/2021 Software-Defined Building 9
Indoor Localization System Framework 3/2/2021 Software-Defined Building 10
Prototype Evaluation • Implemented – Wi. Fi signal strength fingerprint localization – Acoustic Background Spectrum (ABS) fingerprint localization – Kalman Filter for combination • Executed experiments (3 paths) in RADLab in Soda Hall 3/2/2021 Software-Defined Building 11
Evaluation : Wi. Fi Confidence When Confidence is High, Error is Low = When Error is High, Confidence is Low 3/2/2021 Software-Defined Building 12
Evaluation : ABS Confidence When Confidence is High, Error is Low = When Error is High, Confidence is Low Exceptions exist 3/2/2021 Software-Defined Building 13
Evaluation : Kalman Filter Combination Framework leans to choose better estimations and avoids spikes Average performance is the same as the better one 3/2/2021 Software-Defined Building 14
Future Work • Semantic Localization Compared to physical localization • Automatic Data Collection and Map Generation – Crowdsourcing 3/2/2021 Software-Defined Building 15
Prospect • Plug and play indoor localization service in any building • Robust indoor localization services • Provide semantic location information • Generic APIs for application development – Personalized energy usage tracking – Energy saving: HVAC and light control 3/2/2021 Software-Defined Building 16
Thank you! Q&A Kaifei Chen http: //kaifei. me kaifei@cs. berkeley. edu 3/2/2021 Software-Defined Building 17
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