An inertial navigation system (INS) utilizes three orthogonal accelerometers
and gyroscopes to determine platform position, velocity, and orientation. There
are countless applications for INS, including robotics, autonomous platforms,
and the internet of things. Recent research explores the integration of
data-driven methods with INS, highlighting significant innovations, improving
accuracy and efficiency. Despite the growing interest in this field and the
availability of INS datasets, no datasets are available for gyro-free INS
(GFINS) and multiple inertial measurement unit (MIMU) architectures. To fill
this gap and to stimulate further research in this field, we designed and
recorded GFINS and MIMU datasets using 54 inertial sensors grouped in nine
inertial measurement units. These sensors can be used to define and evaluate
different types of MIMU and GFINS architectures. The inertial sensors were
arranged in three different sensor configurations and mounted on a mobile robot
and a passenger car. In total, the dataset contains 35 hours of inertial data
and corresponding ground truth trajectories. The data and code are freely
accessible through our GitHub repository.

研究通过整合数据驱动方法与惯性导航系统（INS），提高准确性和效率。为了填补相关领域的研究空白，设计并记录了无陀螺仪 INS（GFINS）和多惯性测量单元（MIMU）数据集，可用于定义和评估不同类型的 MIMU 和 GFINS 体系结构。数据集包含 35 小时的惯性数据和对应的准确轨迹。