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The attitude estimation system based on vision/inertial fusion is of vital importance and great urgency for unmanned ground vehicles (UGVs) in GNSS-challenged/denied environments. This paper aims to develop a fast vision/inertial fusion system to estimate attitude; which can provide attitude estimation for UGVs during long endurance. The core idea in this paper is to integrate the attitude estimated by continuous vision with the inertial pre-integration results based on optimization. Considering that the time-consuming nature of the classical methods comes from the optimization and maintenance of 3D feature points in the back-end optimization thread, the continuous vision section calculates the attitude by image matching without reconstructing the environment. To tackle the cumulative error of the continuous vision and inertial pre-integration, the prior attitude information is introduced for correction, which is measured and labeled by an off-line fusion of multi-sensors. Experiments with the open-source datasets and in road environments have been carried out, and the results show that the average attitude errors are 1.11° and 1.96°, respectively. The road test results demonstrate that the processing time per frame is 24 ms, which shows that the proposed system improves the computational efficiency.

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A simple systematic calibration method based on acceleration and angular rate measurements is introduced for the fiber-optic gyro strapdown inertial navigation system in this paper. Meanwhile, a unified mathematical framework and an iterative calculation method are designed for the systematic calibration method. Using this method, one can estimate the fiber-optic gyro inertial measurement unit (FOG IMU) parameters both at a manufacturer’s facility and in the field. In order to get all FOG IMU parameters, a procedure adopted based on this approach consists of two stages: First, FOG IMU raw data (accelerometer and gyro readouts) are accumulated in 19 specified FOG IMU positions. Second, the accumulated data are processed by special software to estimate all FOG IMU parameters. In addition, observability analysis of the method in 19 specified FOG IMU positions is done without the limitation of FOG IMU’s initial orientation, and this analysis provides theoretical support for the application in a complex terrain. Moreover, the influence of gravity disturbance is analyzed for the first time. The analysis and experiment results show that the systematic calibration method provided by this work can meet the requirement of FOG IMU calibration.

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