基于双重稳健回归的果树行间可行驶区域识别算法

    Recognition algorithm of drivable area between rows of fruit trees based on double robustness regression

    • 摘要:
      目的  提出一种复杂环境下以天空为背景的果树行间可行驶区域识别算法,以便农业机器人导航系统中工作路径的提取。
      方法  通过蓝色分量(B分量)进行树冠和背景天空的分离,改进Otsu算法实现更好的分割效果,形态学处理后根据树顶分布规律,进行动态阈值“V形”感兴趣区域寻找及特征点提取,使用泰尔−森稳健回归剔除干扰点后,使用随机采样一致性(Random sample consensus,RANSAC)算法进行拟合,得到树顶处直线,通过斜率变换关系得到可行驶区域边缘直线斜率,利用剔除后特征点信息和剔除阈值获得关键点坐标,以斜率为约束条件,代入关键点,得到可行驶区域边缘直线方程,并使用最小二乘法进行拟合,以此实现可行驶区域识别。
      结果  试验结果表明,本文双重稳健回归算法较泰尔−森算法和RANSAC算法平均偏差角度分别减小了8.28%和9.88%,标准差分别减少了6.25%和22.89%,准确率分别提高了4.64%和10.49%。
      结论  研究结果可为农业机器人在大多数标准化果园复杂环境中的可行驶区域识别和路径提取提供研究思路。

       

      Abstract:
      Objective  In order to extract the working path in the agricultural robot navigation system, we proposed an algorithm for identifying the drivable area between rows of fruit trees with the sky as the background in a complex environment.
      Method  The tree crown and the background sky were separated by the blue component (B component), and the Otsu algorithm was improved to achieve a better effect of segmentation. After morphological processing, according to the regularity of tree top distribution, dynamic threshold was used to find “V-shaped” region of interest and extract feature points. After the interference points were eliminated by Theil-Sen robustness regression, the straight line at the tree top was fitted by random sample consensus (RANSAC) algorithm, the slope of the straight line at the edge of the drivable area was obtained through the slope transformation relationship, and the key point coordinates were obtained using the information of the feature points after elimination and the threshold elimination. Taking the slope as the constraint condition, the linear equation of the edge of the drivable area was obtained by substituting the key points. The least square method was used to fit the data for realizing the recognition of the drivable area.
      Result  The experimental results showed that compared with Theil-Sen algorithm and RANSAC algorithm, the average deviation angle of the double robustness regression algorithm in this paper was reduced by 8.28% and 9.88%, the standard deviation was reduced by 6.25% and 22.89%, and the accuracy was improved by 4.64% and 10.49%.
      Conclusion  The research results can provide research ideas for the drivable area recognition and path extraction of agricultural robots in the complex environment of most standardized orchards.

       

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