一种基于图优化的行人协同定位方法

基于手机惯性传感器的行人航位推算方法是行人导航的核心方法之一。 然而由于传感器噪声等因素,航位推算获取 的位置信息误差往往随着时间发散,通常将航位推算和卫星导航通过卡尔曼滤波构成组合导航系统,利用卫星提供的高精度定 位信息补偿航位推算误差。 提出一种基于图优化的行人协同定位方法,将状态转移、量测和协同测距信息都作为状态的约束, 统一进行优化估计。 为验证方法的有效性,分别在卫星信号良好、无卫星环境下进行了实验验证。 实验分析结果表明,基于图 优化的行人协同定位方法在有无卫星信号情况下,都可以有效地提升系统的定位精度。 和基于卡尔曼滤波的协同方法相比,最 大水平定位误差都减少了 30% 以上。

A pedestrian cooperative localization method based on graph optimization

The pedestrian dead reckoning (PDR) algorithm based on the mobile phone inertial sensors is one of the core methods for pedestrian navigation. However, due to factors such as the sensor noise, the positioning error of dead reckoning accumulates over time, which may lead to the divergence of the pedestrian positioning. To compensate the positioning error and improve the positioning accuracy of the PDR method, the GNSS positioning is generally introduced and combined with the conventional PDR method via the Kalman filter. In this article, a pedestrian collaborative positioning method based on the factor graph optimization is proposed. The state transition, measurements and collaborative ranging information are all used as state constraints. In addition, the optimal estimation is performed uniformly. To evaluate the performance of the method, experiments are implemented in both open-sky area and GNSS denied environment. The experimental analysis results show that the pedestrian collaborative positioning method based on the factor graph optimization can effectively improve the positioning accuracy both in open-sky and GNSS degraded area. Compared with the cooperative method based on Kalman filter, the maximum horizontal positioning error is reduced by more than 30% .