基于三目视觉的自主导航拖拉机行驶轨迹预测方法及试验

为了实现自主导航拖拉机离开卫星定位系统时能够持续可靠工作,该文提出了基于三目视觉的拖拉机行驶轨迹预测方法。该方法将三目相机分解为长短基线2套双目视觉系统分时独立工作。通过检测相邻时刻农业环境中同一特征点的坐标变化反推拖拉机在水平方向上的运动矢量,并通过灰色模型预测未来时刻的运动矢量变化,最终建立不同速度下的前进方向误差模型。试验结果表明:拖拉机行驶速度为0.2 m/s时,46.5 s后前进方向误差超过0.1 m,对应行驶距离为9.3 m。行驶速度上升到0.5 m/s时,该时间和行驶距离分别降低到17.2 s和8.6 m。当行驶速度上升到0.8 m/s时,该时间和距离分别快速降低至8.5 s和6.8 m。行驶速度越高,前进方向误差增速越高。该方法可用于短时预测拖拉机的行驶轨迹,为自主导航控制提供依据。

Traveling trajectory prediction method and experiment of autonomous navigation tractor based on trinocular vision

Abstract: In order to make the autonomous navigation tractors work steadily and continuously without the satellite positioning system, a traveling trajectory prediction system and method based on trinocular vision were designed in this paper. The system was composed of a trinocular vision camera, an IEEE 1394 acquisition card and an embedded industrial personal computer (IPC). The right and left sub cameras constituted a binocular vision system with a long base line. The right and middle sub cameras constituted another binocular vision system with a narrow base line. To obtain more precise measurement results, the two binocular vision systems worked independently and in time-sharing. Then the motion vectors of tractor, which were in presentation of horizontal direction data, were calculated by the feature point coordinate changing in the working environment of the tractor. Finally, the error models which were in the direction of heading were established at different velocities, and the motion vectors of tractor were predicted by the models based on grey method. The contrast experiments were completed with a modified tractor of Dongfanghong SG250 at the speed of 0.2, 0.5 and 0.8m/s. During the experiments, the IPC was used to collect RTK-GPS data and predict movement tracks. The RTK-GPS used in the experiments was a kind of high-precision measuring device, and the measuring precision can reach 1-2 cm. Therefore, the location data of RTK-GPS were supposed as the standard which was used to compare with the data from trinocular vision system. The experimental results showed that the method mentioned above could accurately predict the trajectory of the tractor on the plane with an inevitable error which was mainly caused by the visual measurement error of the forward direction (z direction). When the tractor travelled at the speed of 0.2 m/s, the time and the distance that the error in forward direction exceeded 0.1 m equaled 46.5 s and 9.3 m, respectively. When the speed increased to 0.5 m/s, the time and the distance decreased to 17.2 s and 8.6 m, respectively. When the driving speed increased to 0.8 m/s, the time and distance quickly decreased to 8.5 s and 6.8 m, respectively. It showed that the higher the tractor traveling speed, the faster the error in forward direction increased. After that, the relationship between errors in forward direction and traveling time was acquired and analyzed by the way of nonlinear data fitting. In addition, the experimental results showed that the trend of lateral error (x direction) which was perpendicular to forward direction was not regular. When the speed was 0.2 m/s, the average error was 0.002?5 m with a standard deviation (STD) of 0.003?9. When the speed increased to 0.5 m/s and 0.8 m/s, the average error in lateral direction was 0.008?2 m with an STD of 0.012?4 and 0.003?6 m with an STD of 0.006?4. The result showed that the lateral error was very small and almost invariable. Therefore, the errors of trinocular vision were mainly caused by the errors of the forward direction. The root causes of the error were the natural light and time-delay during the image processing. According to the experimental data and results, the system and method proposed in this paper could be used to measure and predict the traveling trajectory of a tractor in the dry agricultural environment with the sudden loss of the satellite signal in a short period of time. The measured and predicted data could provide temporary help for the operations of autonomous tractors.