Year: 2025
Type: conference-paper
Abstract: To reduce heading angle errors in pedestrian localization systems based on inertial measurement units (IMUs) and improve positioning accuracy, this paper proposes an enhanced foot-mounted inertial navigation method with an improved maximum stride length constraint. First, the principle of the pedestrian localization error constraint algorithm based on the dual-foot maximum stride length constraint is introduced. Then, considering the inherent upper limit of single-foot stride length during walking, a pedestrian localization method integrating both single-foot and dual-foot maximum stride length constraints is designed. This method assumes that the current moving foot must satisfy both constraints simultaneously, thereby determining a reasonable foot position. This position is treated as an observation and fused with the inertial navigation solution using a Kalman filter (KF) to mitigate heading angle errors and enhance localization accuracy. Indoor straight-line walking experiments demonstrate that, compared to the zero-velocity update method and the traditional maximum stride length constraint method, the proposed approach reduces pedestrian localization errors from 3.67% and 1.68% of the total travel distance to 0.59%, significantly decreasing the deviation between the estimated trajectory and the actual path.
Authors: Jia Su, Bokai Meng, Qingwu Yi, Haonan Jia
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4413845315