基于测距信息的行人惯性定位方法

A Pedestrian Inertial Positioning Method Based on Ranging Information

摘要:

行人惯性导航系统在解算过程中随时间产生累积误差，使定位结果发散，而使用传统的广义似然比检测（GLRT）的零速修正算法不能抑制定位误差的发散，因此提出了以低成本、高精度检测为目标，设计一种基于对地的超声波测距传感器辅助 GLRT 零速修正的方法（简称：UA-GLRT）。以标准篮球场为实验场地，利用 5 次独立重复性实验，将所设计的 UA-GLRT 算法与 GLRT 算法的有效性进行对比，且通过位置误差和闭环误差对两种零速检测算法进行评估。实验结果表明，UA-GLRT 算法相比于 GLRT 算法，其终点与起点之间的位置误差平均值由 1. 02 m 降至 0. 43 m，闭环误差平均值由 1. 19%D 降至 0. 5%D。

Abstract:

Pedestrian inertial navigation systems accumulate errors over time during computation， leading to divergent positioning results. The traditional Generalized Likelihood Ratio Test （GLRT） zero-velocity correction algorithm is insufficient to suppress the divergence of positioning errors. To address this limitation， this paper proposes a low-cost， high-precision detection method based on an ultrasonic ranging sensor to assist the GLRT zero-velocity correction （abbreviated as UA-GLRT）. Using a standard basketball court as the experimental site， five independent repeatability experiments were conducted to compare the effectiveness of the UA-GLRT algorithm with that of the GLRT algorithm. The two zero-velocity detection algorithms were evaluated through position errors and loop closure errors. Experimental results demonstrate that the UA-GLRT algorithm reduced the average start-end position error from 1. 02 m to 0. 43 m and decreased the average loop closure error from 1. 19%D to 0. 5%D compared to the baseline GLRT method.