基于Harris和卡尔曼滤波的农业机器人田间稳像算法

针对田间颠簸环境影响农业机器人采集实时稳定图像问题，提出了基于Harris和卡尔曼滤波的农业机器人田间稳像算法。首先，利用摄像头获取田间抖动视频图像序列，进行图像子区域划分并计算各区域灰度均方差，进而确定各区域Harris角点阈值；通过自适应角点阈值设置，增加角点距离约束，完成图像角点检测。然后，对检测出的角点进行光流跟踪，计算出帧间运动估计参数。最后，利用自适应卡尔曼滤波算法对运动估计参数进行平滑操作并动态调整滤波平滑性能，获得精确运动估计矢量。测试结果表明，改进后的Harris角点检测算法区域平均分布标准差减小；自适应卡尔曼滤波算法在保证平滑随机运动前提下，跟踪主动运动性能平均提升30.75个百分点；稳像后的图像峰间信噪比提升15.93%,单帧处理时间为25.66 ms,满足农业机器人30 f/s高速图像采集时同步稳像对实时性要求。

Field Image Stabilization Algorithm for Agricultural Robot Based on Harris and Kalman Filter

Aiming to solve the problem of image jitter of the tracked agricultural robot on the bumpy road in the field. Firstly, the field jitter video image sequence was obtained by the camera, the image was divided into molecular regions, and the mean square error of gray value of each region was calculated, and then the Harris corner threshold of each region was determined, and the adaptive corner threshold was set. The corner distance constraint was added to complete the corner detection of the image. Secondly, optical flow tracking was performed on the detected corners, and the parameters of interframe motion estimation were calculated. Finally, the motion estimation parameters were smoothed by the adaptive Kalman filter algorithm, and the smoothing performance of the filter was dynamically adjusted to obtain the accurate motion estimation vector. The experimental results showed that the improved Harris corner detection algorithm reduced the standard deviation of the average distribution of the region. Under the premise of ensuring smooth random motion, the tracking performance of active motion of adaptive Kalman filter was improved by 30.75 percentage points. After image stabilization, the signal to noise ratio of the image was improved by 15.93%, and the processing time of single frame was 25.66ms, which can meet the real time processing at the acquisition rate of 30f/s. The traditional Harris corner detection algorithm was improved to overcome the phenomenon of uneven corner distribution and easy clustering. An adaptive Kalman filter algorithm was proposed to suppress the random motion of the camera and improve the performance of tracking the active motion of the camera, which had a good image stabilization performance in tracked agricultural robots.