基于平滑轮廓对称轴法的苹果目标采摘点定位方法

Localization method of picking point of apple target based on smoothing contour symmetry axis algorithm

果实采摘点的精确定位是采摘机器人必须解决的关键问题。鉴于苹果目标具有良好对称性的特点,利用转动惯量所具有的平移、旋转不变性及其在对称轴方向取得极值的特性,提出了一种基于轮廓对称轴法的苹果目标采摘点定位方法。为了解决分割后苹果目标边缘不够平滑而导致定位精度偏低的问题,提出了一种苹果目标轮廓平滑方法。为了验证算法的有效性,对随机选取的20幅无遮挡的单果苹果图像分别利用轮廓平滑和未进行轮廓平滑的算法进行试验,试验结果表明,未进行轮廓平滑算法的平均定位误差为20.678°,而轮廓平滑后算法平均定位误差为4.542°,比未进行轮廓平滑算法平均定位误差降低了78.035%,未进行轮廓平滑算法的平均运行时间为10.2ms,而轮廓平滑后算法的平均运行时间为7.5ms,比未进行轮廓平滑算法平均运行时间降低了25.839%,表明平滑轮廓算法可以提高定位精度和运算效率。利用平滑轮廓对称轴算法可以较好地找到苹果目标的对称轴并实现采摘点定位,表明将该方法应用于苹果目标的对称轴提取及采摘点定位是可行的。

The localization of picking points of fruits is one of the key problems for picking robots, and it is the first step of implementation of the picking task for picking robots. In view of a good symmetry of apples, and characteristics of shift, rotation invariance, and reaching the extreme values in symmetry axis direction which moment of inertia possesses, a new method based on a contour symmetry axis was proposed to locate the picking point of apples. In order to solve the problem of low localization accuracy which results from the rough edge of apples after segmentation, a method of smoothing contour algorithm was presented. The steps of the algorithm were as follow, first, the image was transformed from RGB color space intoL＊a＊b color space, and then K-means color clustering algorithm was used to detect the apple target. The image was processed with amorphological opening operation with a ‘disk’-shaped structural element of radius 5 beforeK-means clustering algorithm so as to ensure the accuracy of theK-means algorithm. Secondly, image pre-processing algorithms were carried out. Hole filling and area threshold algorithms were performed first to remove noise, and then a mathematical morphology operation with a ‘disk’-shaped structural element of radius 10 was conducted to remove big spurs on the contour of apples. Thirdly, the contour of an apple was extracted by processing the pre-processed image with a morphological open operation. The calculate centroid of an apple and the distance between contour points and centroid were calculated, and then the distance curve could be obtained. After that, wavelet decomposition and Spline interpolation algorithms were used to smooth the distance curve, and then the smoothed distance curve was used to rebuild the contour of the apple. The procedures of rebuilding the contour of apples were as follow： 1） Coordinates transformation. In order to make an image coordinates system in accordance with common used coordinates system, coordinates transformation was needed. 2） Translation of the original point of coordinates to simply calculation. 3） Contour points-centroid angle normalization and calculation, which was of great significance to rebulit contour points. 4） Rebuilt contour points using the smoothed distance curve and normalized contour points-centroid angle. After these four steps, the contour of an apple could be obtained. Finally, the contour was used to extract the symmetry axis of an apple by using a moment of inertia algorithm. In order to verify the validity of this algorithm, a test was conducted by using the original algorithm and the presented algorithm with 20 single and unblocked apple images, respectively. The average error of the original algorithm was 20.678°, and the average error of the presented algorithm was 4.542°, 78.035% less than that of the original algorithm. Furthermore, the average run-time of the proposed algorithm was 7.5 ms, which was decreased by 25.839% when compared to the original algorithm （10.2 ms）. The results showed that the presented algorithm could locate the picking point of an apple accurately and effectively. In conclusion, the presented algorithm is feasible for extracting the symmetry axis and locating the picking point of apples. However, this method was not applicable to blocked apple images, uneven illumination apple image, images containing apples with poor symmetry, and apples with part of a green region, for the entire contour of apple in these images cannot be obtained.