Improving the error drift of inertial navigation based indoor location tracking

Inertial sensing based indoor localization currently requires fairly precise layout maps, to help provide constraints and landmarks that bound the error drift. In this paper, we seek to improve the accuracy of one component of inertial-based tracking, namely the estimation of an individual's stride-length, so as to reduce the cumulative drift. We show that an individual's stride-length is affected by both his/her movement speed and heading-changes in the trajectory, and present an adaptive, online stride-length estimation algorithm that learns appropriate stride-length distributions for different (speed, heading) combinations. Initial experiments conducted using our proposed approach in combination with state-of-the-art step counting and heading estimation techniques, reduce the 95th percentile of average localization error by ≈ 30%. We thus envisage that inertial tracking may become practical even with coarse-grained map information.