ActionSLAM: Using location-related actions as landmarks in pedestrian SLAM

Indoor localization at minimal deployment effort and with low costs is relevant for many ambient intelligence and mobile computing applications. This paper presents ActionSLAM, a novel approach to Simultaneous Localization And Mapping (SLAM) for pedestrian indoor tracking that makes use of body-mounted sensors. ActionSLAM iteratively builds a map of the environment and localizes the user within this map. A foot-mounted Inertial Measurement Unit (IMU) keeps track of the user's path, while observations of location-related actions (e.g. door-opening or sitting on a chair) are used to compensate for drift error accumulation in a particle filter framework. Location-related actions are recognizable from body-mounted IMUs that are often used in ambient-assisted living scenarios for context awareness. Thus localization relies only on on-body sensing and requires no ambient infrastructure such as Wi-Fi access points or radio beacons. We characterize ActionSLAM on a dataset of 1.69km walking in three rooms and involving 241 location-related actions. For the experimental dataset, the algorithm robustly tracked the subject with mean error of 1.2m. The simultaneously built map reflects the building layout and positions landmarks with a mean error of 0.5m. These results were achieved with a simulated action recognition system consisting of an IMU attached to the wrist of a user and a smartphone in his pocket. We found that employing more complex action recognition is not beneficial for ActionSLAM performance. Our findings are supported by evaluations in synthetic environments through simulation of IMU signals for walks in typical home scenarios.