The recent indoor localization techniques use inertial sensors for position estimations in order to obtain a certain degree of freedom from infrastructure based solutions. Unfortunately, this dependency cannot be completely eliminated due to the cumulative errors induced in the localization process. While many methods are designed to reduce the required number of reference points, completely infrastructure independent solutions are still missing. In this paper we extend the approach of DREAR, a mobile-based context-aware indoor localization framework. DREAR exploits the ability to recognize certain human motion patterns with a smartphone, representing activities related to walking, climbing stairs, taking escalators, etc. This allows the detection of corridors, staircases and escalators, knowledge which can be used to create building interior related reference points. Based on these a scenario specific context interpreter controls the localization process and provides position refinement for the elimination of the cumulated errors. However, due to the cumulated errors in the trajectory, in case of neighbouring reference points with similar characteristics an adequate distinction cannot made, based solely on the detected activities, which leads to wrong reference point associations and erroneous location refinements. Thus, we extended DREAR with a trajectory reconstruction algorithm, to cope with these errors and their effect on the outcome of reference point selection. The proposed solution is evaluated in a complex subway scenario, its performance is analysed focusing on path reconstructions and the benefits of using specific context-related information. The results are promising, the proposed algorithm presents further improvements relative to the performance of DREAR, providing an excellent localization and path reconstruction solution.
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- TREKIE - Ubiquitous Indoor Localization with Trajectory REconstruction Based on Knowledge Inferred from Environment
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