@INPROCEEDINGS{8968519,
  author={Y. {Yu} and W. {Gao} and C. {Liu} and S. {Shen} and M. {Liu}},
  booktitle={2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, 
  title={A GPS-aided Omnidirectional Visual-Inertial State Estimator in Ubiquitous Environments}, 
  year={2019},
  volume={},
  number={},
  pages={7750-7755},
  abstract={The visual-inertial navigation system (VINS) has been a practical approach for state estimation in recent years. In this paper, we propose a general GPS-aided omnidirectional visual-inertial state estimator capable of operating in ubiquitous environments and platforms. Our system consists of two parts: 1) the pre-processing of omnidirectional cameras, IMU, and GPS measurements, and 2) the sliding window based nonlinear optimization for accurate state estimation. We test our system in different conditions including an indoor office, campus roads, and challenging open water surface. Experiment results demonstrate the high accuracy of our approach than state-of-the-art VINSs in all scenarios. The proposed odometry achieves drift ratio less than 0.5% in 1200 m length outdoors campus road in overexposure conditions and 0.65% in open water surface, without a loop closure, compared with a centimeter accuracy GPS reference.},
  keywords={},
  doi={10.1109/IROS40897.2019.8968519},
  ISSN={2153-0866},
  month={Nov},}