Indoor Localization Without a Prior Map by Trajectory Learning From Crowdsourced Measurements

Accommodation of a situation when a prior map is not available in an indoor localization system is valuable to cost-effective operations by removing a need for map drawing and map updating. This paper suggests a trajectory learning method using crowdsourced measurements in order to support the absence of map. A localization framework based on a particle filter is formalized by machine-learning-based feature extraction and Gaussian process (GP) regression. The feature extraction algorithm reduces dimensionality of sparse measurement vector, and it is applied to detect floor level and designated landmarks. Also, the combination of the feature extraction and the GP regression is used for modeling nonlinear relationship between location and measurement. By this combination, locations of Wi-Fi access points are not required to be known. From the field experimental results, we confirm that the detections of floor level and landmarks are accurate, the learned trajectories are close to the true map, and positioning accuracy is improved thanks to the learning-aided localization.