In this paper, we present an efficient and robust GPS-aided visual inertial odometry (GPS-VIO) system that fuses IMU-camera data with intermittent GPS measurements. To perform sensor fusion, spatiotemporal sensor calibration and initialization of the transform between the sensor reference frames are required. We propose an online calibration method for both the GPS-IMU extrinsics and time offset as well as a reference frame initialization procedure that is robust to GPS sensor noise. In addition, we prove the existence of four unobservable directions of the GPS-VIO system when estimating in the VIO reference frame, and advocate a state transformation to the GPS reference frame for full observability. We extensively evaluate the proposed approach in Monte-Carlo simulations where we investigate the system’s robustness to different levels of GPS noise and loss of GPS signal, and additionally study the hyper-parameters used in the initialization procedure. Finally, the proposed system is validated in a large-scale real-world experiment.
Title: Intermittent GPS-aided VIO: Online Initialization and Calibration
Authors: Woosik Lee, Kevin Eckenhoff, Patrick Geneva, and Guoquan Huang