Research on posture estimation method of small-size vehicle in the ground test based on the graph optimal model

In the ground test of multi-vehicle, the global motion estimation method based on graph optimization model was proposed in order to reduce the cumulative error of posture estimation due to the discontinuity of tracking trajectory caused by tracking algorithm. Firstly, a ground test vision motion estimation system for vehicle was established. Then, according to the number of feature points on the vehicle, a vector crossover method was proposed to solve the position of vehicle posture, and the estimation value of vehicle posture with good data correlation was obtained. The intermediate coordinate system method was used to solve the value of the initial position node of the track segment under the measuring coordinate system, and finally, under the framework of graph optimization theory, the nonlinear global optimization of the motion estimation results of the vehicle during the whole measurement process was carried out to reduce the cumulative error of the linear algorithm, and the feasibility and precision of the vehicle motion estimation algorithm were verified by simulation and practical experiments. The experimental results show that the accuracy of the proposed vehicle attitude estimation algorithm can reach 0.5° (3σ) and 3 mm (3σ) respectively, and the actual absolute measurement accuracy can reach 1.3° (3σ) and 4 mm (3σ) respectively, the size of the vehicle is 400 mm in the range of the measuring range of 6 000 mm×6 000 mm×3 000 mm and the three-dimensional positioning accuracy of the feature points is 2.9 mm. It basically meets the requirements of ground test for the development of multi-vehicle formation algorithm and the performance evaluation of guidance control system, stable and reliable, high precision and strong anti-jamming ability.