Multi-Condition Constrained Pedestrian Localization Algorithm Based on IMU

The MEMS inertial sensors based on the pedestrian localization system assisted by the zero-velocity update (ZUPT) algorithm has gained widespread attention, due to its effective independent localization in indoor environments. However, in the realistic pedestrian localization test, the system often appears to drift because of the long-term error accumulation of inertial sensors and the limitation of the error suppression of traditional pedestrian localization algorithms. In this article, based on full analysis of existing constraint-based methods, a multi-condition constrained pedestrian localization algorithm is proposed, which integrates zero velocity detection based on phase threshold constraint, single and dual feet fusion constraint algorithms, to suppress drift and improve localization accuracy. The experimental results demonstrate that the multi-condition constraint algorithm can reduce localization errors by 59% compared to the unconstrained approach, and by 42% and 26% compared to algorithms using only single-foot or dual-feet constraints, respectively. The trajectory generated from the experiments further shows that the proposed algorithm produces a trajectory that more closely aligns with the actual walking path.