Indoor PDR Trajectory Matching by Gyroscope and Accelerometer Signal Sequence Without Initial Motion State

In recent years, pedestrian dead reckoning (PDR) is considered a popular localization solution for its reliable accuracy. To tackle its intrinsic problem of accumulative errors, various methods are proposed to refine the trajectory by indoor space information to promote localization accuracy. However, these methods usually require the initial motion state, such as starting location and absolute orientation to infer the coordinates. They may be limited because the initial location has to be determined by other highly precise sensors and required people holding the phone ahead in the fixed position to ensure the
${x}$
-axis is consistent with the heading. In this article, we release the constraints by utilizing the gyroscope and accelerometer sensor sequence to estimate the orientation change and trajectory length, which are insensitive to the initial motion state, to build a hidden Markov model (HMM) model to infer the initial motion and refine the trajectory by spatial information. First, the indoor environment is divided into grids to construct a directed map. Then, we use the gyroscope and accelerometer to collect the signal sequence to represent the rotation angle and length information of the trajectory. Finally, the Viterbi algorithm is used for the inference of the whole trajectory including the initial location and orientation. The experiments show that the initial location and absolute orientation can be predicted as the convergence of the matching candidates when the length of the trajectory increases. And the localization error of the proposed method is superior to the compared method with the benefit of rotation angle and trajectory length.