FastSLAM: a factored solution to the simultaneous localization and mapping problem
Eighteenth national conference on Artificial intelligence
Pedestrian Tracking with Shoe-Mounted Inertial Sensors
IEEE Computer Graphics and Applications
Pedestrian localisation for indoor environments
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
Location and Navigation Support for Emergency Responders: A Survey
IEEE Pervasive Computing
Survey of Wireless Indoor Positioning Techniques and Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Activity-Based Estimation of Human Trajectories
IEEE Transactions on Robotics
Smartphone-based pedestrian tracking in indoor corridor environments
Personal and Ubiquitous Computing
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We present an indoor tracking system based on two wearable inertial measurement units for tracking in home and workplace environments. It applies simultaneous localization and mapping with user actions as landmarks, themselves recognized by the wearable sensors. The approach is thus fully wearable and no pre-deployment effort is required. We identify weaknesses of past approaches and address them by introducing heading drift compensation, stance detection adaptation, and ellipse landmarks. Furthermore, we present an environment-independent parameter set that allows for robust tracking in daily-life scenarios. We assess the method on a dataset with five participants in different home and office environments, totaling 8.7h of daily routines and 2500m of travelled distance. This dataset is publicly released. The main outcome is that our algorithm converges 87% of the time to an accurate approximation of the ground truth map (0.52m mean landmark positioning error) in scenarios where previous approaches fail.