褚福春, 宫金良, 张彦斐. 基于密度自适应的RANSAC非结构化环境下果园机器人导航[J]. 华南农业大学学报, 2022, 43(5): 99-107. DOI: 10.7671/j.issn.1001-411X.202111025
    引用本文: 褚福春, 宫金良, 张彦斐. 基于密度自适应的RANSAC非结构化环境下果园机器人导航[J]. 华南农业大学学报, 2022, 43(5): 99-107. DOI: 10.7671/j.issn.1001-411X.202111025
    CHU Fuchun, GONG Jinliang, ZHANG Yanfei. Orchard robot navigation in unstructured environment based on density adaptive RANSAC[J]. Journal of South China Agricultural University, 2022, 43(5): 99-107. DOI: 10.7671/j.issn.1001-411X.202111025
    Citation: CHU Fuchun, GONG Jinliang, ZHANG Yanfei. Orchard robot navigation in unstructured environment based on density adaptive RANSAC[J]. Journal of South China Agricultural University, 2022, 43(5): 99-107. DOI: 10.7671/j.issn.1001-411X.202111025

    基于密度自适应的RANSAC非结构化环境下果园机器人导航

    Orchard robot navigation in unstructured environment based on density adaptive RANSAC

    • 摘要:
      目的  提出一种基于多传感器融合的果园导航方案,解决果园机器人在GPS导航过程中受果树遮挡导致信号弱、定位效果差的问题。
      方法  通过16线激光雷达采集高精度的三维点云数据,利用Voxel grid filter滤波算法进行点云预处理,降低点云密度并去除离散点,将果树行通过欧几里类算法进行聚类,采用改进的随机采样一致性 (Random sample consensus, RANSAC) 算法拟合出果树行直线,根据平行直线的关系,推算得到导航线,并融合惯性测量单元(Inertial measurement unit, IMU)对果园机器人进行高精度定位。基于差速转向和纯追踪模型进行轨迹跟踪,实现果园机器人在果树行间自主导航以及自动换行的目标。
      结果  在将激光雷达和IMU的数据进行融合后,获取到果园机器人的准确位姿,当机器人以速度0.8 m/s在果园作业时,对比最小二乘法和传统RANSAC法产生的偏差,基于密度自适应RANSAC法产生的横向偏差不超过0.1 m、航向角偏差不超过1.5°,均为3种方法中的最小值。但当机器人速度增加到1.0 m/s时,各项偏差均明显增大。
      结论  本文提出的基于多传感器融合的果园机器人导航技术适用于大多数规范化果园,具有重要推广价值。

       

      Abstract:
      Objective  An orchard navigation scheme based on multi-sensor fusion was proposed to solve the problems of weak signal and poor positioning effect caused by tree occlusion in the GPS navigation process of orchard robot .
      Method  High-precision 3D point cloud data were collected by 16-line lidar, point cloud was preprocessed by Voxel grid filter algorithm, point cloud density was reduced and discrete points were removed, fruit tree rows were clustered by Euclidian algorithm, and the straight lines of fruit tree rows were fitted by improved random sampling consistency (RANSAC) algorithm. According to the relationship of parallel lines, the navigation line was calculated and integrated with inertial measurement unit (IMU) for high-precision positioning of orchard robot. Based on differential steering and pure tracking model, the goal of autonomous navigation and automatic line wrapping of orchard robot was realized.
      Result  After the data fusion of lidar and IMU, the accurate position and pose of the robot were obtained. Compared with the deviation produced by the least square method and the traditional RANSAC method, the lateral deviation based on density adaptive RANSAC method was less than 0.1 m and the heading angle deviation was less than 1.5° when the robot was operating in the orchard at the speed of 0.8 m/s. The deviations were the minimum in the three methods. However, when the robot speed increased to 1.0 m/s, all the deviations increased obviously.
      Conclusion  The orchard robot navigation technology based on multi-sensor fusion proposed in this paper is suitable for most standardized orchards and has important promotion value.

       

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