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Surveyors use Global Positioning System (GPS) technology to measure the precise location and elevation of points on Earth. In a recent survey, GPS receivers were used to determine the coordinates and elevations of two park monuments. The process involved careful mission planning, data collection, and correction to ensure accuracy. The survey began with mission planning to identify optimal satellite visibility and minimize Position Dilution of Precision (PDOP). A geodetic control point...
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Related Experiment Video

Updated: Dec 26, 2025

Using a Real-Time Locating System to Measure Walking Activity Associated with Wandering Behaviors Among Institutionalized Older Adults
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Adaptive Indoor Area Localization for Perpetual Crowdsourced Data Collection.

Marius Laska1, Jörg Blankenbach1, Ralf Klamma2

  • 1Geodetic Institute and Chair for Computing in Civil Engineering & Geo Information Systems, RWTH Aachen University, Mies-van-der-Rohe-Str. 1, 52074 Aachen, Germany.

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Accurate indoor localization relies on quality training data. This study introduces a dynamic area segmentation method for crowdsourced data, improving reliability over exact positioning, especially with imperfect data.

Keywords:
area localizationcrowdsourcingdeep learningfingerprintingindoor localization

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Area of Science:

  • Indoor localization
  • Wireless sensor networks
  • Machine learning

Background:

  • Fingerprinting-based indoor localization accuracy depends on training data quality.
  • Crowdsourced data collection offers continuous updates but introduces data heterogeneity and performance issues.
  • Static area segmentation fails with evolving, unbalanced crowdsourced data.

Purpose of the Study:

  • To develop a data-aware floor plan segmentation algorithm for evolving crowdsourced data.
  • To create a metric balancing classifier expressiveness and performance.
  • To enable adaptive area localization models that improve reliability.

Main Methods:

  • Proposed a novel algorithm for data-aware floor plan segmentation.
  • Developed a selection metric for area classifiers focusing on information gain and classification accuracy.
  • Utilized supervised deep learning for training area classifiers.
  • Implemented a system for regularly updating area localization models.

Main Results:

  • The proposed data-aware segmentation adapts to unbalanced, time-evolving training data distributions.
  • The developed metric effectively balances classifier expressiveness and performance.
  • Adaptive area localization models demonstrated higher reliability than exact position estimation models.

Conclusions:

  • Data-aware segmentation is crucial for effective area localization with crowdsourced data.
  • Adaptive models outperform static approaches in dynamic environments.
  • Area localization offers more reliable positioning guarantees than exact methods in imperfect data settings.