In this study, we built a monocular visual inertial odometry (VIO) dataset with a proposed self-calibrating platform for an inertial measurement unit (IMU). For a learning-based method, lack of database is the biggest limitation. One goal of this study is to generate a high-quality VIO database. Meanwhile, a properly calibrated inertial measurement unit (IMU) is critical for improving VIO system accuracy and precision. However, a professional IMU calibration tool is usually expensive and large. The proposed self calibrating platform shows its benefits in eliminating the manual work in calibration and reducing the cost of the expensive platform, which brings more opportunities to students and researchers. This self-calibrating system was useful for collecting monocular visual inertial odometry datasets while driving. . The calibrated IMU generates 14 numbers at 100Hz: time, 3-axis accelerometer measurements, 3-axis gyroscope measurements, 3 Euler angles, and 4 quaternions. This VIO system is composed of a monocular camera, GoPro Hero 3, an IMU, BNO055, and a GPS, ANT-555. The sensors are mounted on a John Deere Gator™ Full-Size Crossover Utility Vehicle and are operated on an Intel NUC, a mini-size computer. The final dataset contains monocular images, IMU measurements, and GPS outputs.
展开▼
