Active Learning with Data Distribution Shift Detection for Updating Localization Systems

Maintaining smartphone indoor localization systems operational and accurate is challenging. Landmark and WiFi based systems rely on up-to-date reference points and radio maps. These are expensive to refresh for most current systems because of the exhaustive site surveys these rely on. Any physical change in the environment may cause unpredictable transformation in the radio propagation medium or it may affect the typical navigation paths inside the building. Active Learning (AL) involves human intervention in annotating sensor samples collected from the target environment. However, the burden on users to perform this manual task should be minimized to make the approach more user-friendly. We propose a solution that improves AL by involving the users only when sensor data deviates from what is expected at a location. Our solution analyses the confidence of location estimations in the environment. We propose a robust data distribution shift detector that looks at several levels in our location estimation neural network to determine the confidence of predictions. During the training stage, the model builds high confidence for estimations in the areas where data is available. We show that data from outside the training distribution is detected as anomaly, which triggers the AL to prompt the user for her current location as label. These annotated samples and other augmented data are then used to update the location estimation model.