Pose Invariant Activity Classification for Multi-floor Indoor Localization

Smartphone based indoor localization caught massive interest of the localization community in recent years. Combining pedestrian dead reckoning obtained using the phone´s inertial sensors with the Graph SLAM (Simultaneous Localization and Mapping) algorithm is one of the most effective approaches to reconstruct the entire pedestrian trajectory given a set of visited landmarks during movement. A key to Graph SLAM-based localization is the detection of reliable landmarks, which are typically identified using visual cues or via NFC tags or QR codes. Alternatively, human activity can be classified to detect organic landmarks such as visits to stairs and elevators while in movement. We provide a novel human activity classification framework that is invariant to the pose of the smartphone. Pose invariant features allow robust observation no matter how a user puts the phone in the pocket. In addition, activity classification obtained by an SVM (Support Vector Machine) is used in a Bayesian framework with an HMM (Hidden Markov Model) that improves the activity inference based on temporal smoothness. Furthermore, the HMM jointly infers activity and floor information, thus providing multi-floor indoor localization. Our experiments show that the proposed framework detects landmarks accurately and enables multi-floor indoor localization from the pocket using Graph SLAM.