ジャイロフリー慣性航法と複数IMUのためのデータセット
Multiple and Gyro-Free Inertial Datasets
54個の慣性センサを9つのIMUにまとめ、移動ロボットと乗用車に搭載して35時間分の慣性データと正解軌道を収録し、ジャイロフリー慣性航法や複数IMU構成の研究用データセットを公開した。
著者: Zeev Yampolsky, Yair Stolero, Nitzan Pri-Hadash, Dan Solodar, Shira Massas, Itai Savin, Itzik Klein
分類: eess.SP, cs.LG, cs.RO
原文アブストラクト
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.
