SLAC: Calibration-Free Pedometer-Fingerprint Fusion for Indoor Localization

To improve the accuracy of fingerprint-based localization, one may fuse step counter with fingerprints. However, the walking step model may vary among people. Such user heterogeneity may lead to measurement error in walking distance. Previous works often require a step counter tediously calibrated offline or through explicit user input. Besides, as device heterogeneity may introduce various signal readings, these studies often need to calibrate the fingerprint RSSI model. Many of them have not addressed how to jointly calibrate the above heterogeneities and locate the user. We propose SLAC, a novel system which simultaneously localizes the user and calibrates the sensors. SLAC works transparently, and is calibration-free with heterogeneous devices and users. Its novel formulation is embedded with sensor calibration, where location estimations, fingerprint signals, and walking motion are jointly optimized with resultant consistent and correct model parameters. To reduce the localization search scope, SLAC first maps the target to a coarse region (say, floor) via stacked denoising autoencoders and then executes the fine-grained localization. Extensive experimental trials at our campus and the international airport further confirm that SLAC accommodates device and user heterogeneity, and outperforms other state-of-the-art fingerprint-based and fusion algorithms by lower localization errors (often by more than 30 percent).