SmartLoc: Sensing Landmarks Silently for Smartphone Based Metropolitan Localization
Cheng Bo, Xiang-Yang Li, Taeho Jung, Xufei Mao

TL;DR
SmartLoc is a smartphone localization system that combines inertial sensors, GPS, landmarks, and driving patterns to improve accuracy in urban environments, especially when GPS signals are weak.
Contribution
SmartLoc introduces a novel method integrating inertial sensors, GPS, landmarks, and driving patterns with linear regression for enhanced urban localization accuracy.
Findings
Achieves approximately 20m error for 90% of the time.
Significantly outperforms GPS alone with a lower mean error.
Demonstrates effectiveness in city environments.
Abstract
We present \emph{SmartLoc}, a localization system to estimate the location and the traveling distance by leveraging the lower-power inertial sensors embedded in smartphones as a supplementary to GPS. To minimize the negative impact of sensor noises, \emph{SmartLoc} exploits the intermittent strong GPS signals and uses the linear regression to build a prediction model which is based on the trace estimated from inertial sensors and the one computed from the GPS. Furthermore, we utilize landmarks (e.g., bridge, traffic lights) detected automatically and special driving patterns (e.g., turning, uphill, and downhill) from inertial sensory data to improve the localization accuracy when the GPS signal is weak. Our evaluations of \emph{SmartLoc} in the city demonstrates its technique viability and significant localization accuracy improvement compared with GPS and other approaches: the error is…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsIndoor and Outdoor Localization Technologies · Human Mobility and Location-Based Analysis · Context-Aware Activity Recognition Systems
