University of Toronto Foot-Mounted Inertial Navigation Dataset

This dataset consists of measurements from a foot-mounted inertial measurement unit (IMU). In total, we provide data from five different test subjects travelling over more than 7.6 km. The data are combined with various forms of ground truth positioning information that can be used to evaluate the accuracy of a zero-velocity-aided, foot-mounted inertial navigation system (INS). In contrast to other similar datasets, our data incorporate several non-walking motions, such as running, crawling, and stair-climbing, as well as mixed-motion trials in which the test subjects alternated between walking and running. In addition to the dataset itself, we also provide an open source foot-mounted INS, with several zero-velocity detector implementations already coded up. The Python-based INS (called PyShoe), along with a suite of data processing tools, is available in the provided Github repository.本数据集包含足部穿戴式惯性测量单元（Inertial Measurement Unit，IMU）采集的测量数据。本数据集共收录5名不同测试对象的移动数据，总移动里程超过7.6千米。该数据集搭配多种形式的真值定位信息，可用于评估零速辅助型足部穿戴式惯性导航系统（Inertial Navigation System，INS）的定位精度。与其他同类数据集相比，本数据集涵盖多种非行走运动场景，例如跑步、爬行与爬楼梯，同时包含测试对象在行走与跑步之间交替进行的混合运动测试。除数据集本身外，我们还提供一款开源的足部穿戴式惯性导航系统（INS），已内置多种零速检测器的实现代码。这款基于Python开发的惯性导航系统（命名为PyShoe）搭配整套数据处理工具，可在本次提供的GitHub仓库中获取。

搜集汇总

背景与挑战

背景概述

该数据集包含脚戴惯性测量单元（IMU）的测量数据，来自五个测试对象，总距离超过7.6公里，并提供地面真实定位信息以评估惯性导航系统（INS）。其特点是涵盖了跑步、爬行和爬楼梯等非行走动作，以及行走和跑步混合的试验，并附带开源INS工具PyShoe，便于数据分析和系统基准测试。