Research Article Pedestrian Dead Reckoning Navigation with the Help of A ∗ -Based Routing Graphs in Large Unconstrained Spaces F. Taia Alaoui, David Betaille, and Valerie Renaudin IFSTTAR, COSYS, GEOLOC, 44344 Bouguenais, France Correspondence should be addressed to F. Taia Alaoui; fadoua.taia-alaoui@ifsttar.fr Received 10 March 2017; Revised 8 May 2017; Accepted 5 June 2017; Published 10 July 2017 Academic Editor: Carlo Fischione Copyright © 2017 F. Taia Alaoui et al. Tis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. An A ∗ -based routing graph is proposed to assist PDR indoor and outdoor navigation with handheld devices. Measurements are provided by inertial and magnetic sensors together with a GNSS receiver. Te novelty of this work lies in providing a realistic motion support that mitigates the absence of obstacles and enables the calibration of the PDR model even in large spaces where GNSS signal is unavailable. Tis motion support is exploited for both predicting positions and updating them using a particle flter. Te navigation network is used to correct for the gyro drif, to adjust the step length model and to assess heading misalignment between the pedestrian’s walking direction and the pointing direction of the handheld device. Several datasets have been tested and results show that the proposed model ensures a seamless transition between outdoor and indoor environments and improves the positioning accuracy. Te drif is almost cancelled thanks to heading correction in contrast with a drif of 8% for the nonaided PDR approach. Te mean error of fltered positions ranges from 3 to 5 m. 1. Introduction Pedestrian Dead Reckoning (PDR) is widely adopted in the feld of pedestrian navigation with handheld devices. It is particularly adapted to smartphone-based localization as inertial sensors can be designed in a MEMS (Micro- electromechanical Sensors) technology, enabling them to be embedded in lightweight devices. Unlike GNSS receivers, inertial sensors are especially useful indoors as they allow standalone localization without sky visibility. Yet, due to gyro drif and step detection limitations, additional information is required to assist the PDR positioning process. For foot- mounted sensors, zero velocity update (ZUPT) calibration is exploited to adjust the positioning parameters by detecting stance phases within the gait cycle (static phase), though this calibration is not possible with handheld devices because of free hand motion and an increased difculty to detect the stance phase. Outdoors, PDR can still be aided by GNSS [1], but this is not feasible indoors because of signal unavailabil- ity and further measurements are needed. Tese could be provided by radio beacons or visual information. Te frst approach requires infrastructure deployment and training [2], while the second necessitates a camera and further image processing for feature recognition [3]. A third possibility is to constrain the pedestrian’s position using map information. Two main paradigms can be retained from previous work. Either walkable space is given by 2D maps delimited by obstacles [4] or it is given by a routing graph network that transforms the positioning process into a piecewise 1D model [5]. In the frst case, space is better explored but the map is not exploited further than for detecting static obstacles (e.g., walls). Tis means that no calibration is performed unless an obstacle is hit. On the contrary, routing graphs are much more constraining because the motion model is directly given by the graph network. Hence, their use has greater impact on the shape and accuracy of the trajectory and they have to be realistic enough to limit positioning errors. Tis paper focuses on routing graph-assisted PDR. In fact, routing graphs involve a simple motion model that allows both obstacle avoidance and the calibration of walking directions within straight line travels [5]. Teir use can even be extended to calibrating the step length model as reported in our previous work [6], though two major drawbacks make their use quite impractical and sometimes inefective. Hindawi Wireless Communications and Mobile Computing Volume 2017, Article ID 7951346, 10 pages https://doi.org/10.1155/2017/7951346