Fast Attitude Estimation System for Unmanned Ground Vehicle Based on Vision/Inertial Fusion

Abstract

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.