基于离散因子的多传感器数据融合和导航线提取方法

Multi-sensor data fusion and navigation line extraction method based on discrete factor

【目的】针对玉米田间路径边界模糊和形状不规则特点,普通的田间导航线提取算法在农业机器人实际应用中会出现偏差过大的问题,本文针对3~5叶期玉米田提出了基于离散因子的相机与三维激光雷达融合的导航线提取算法。【方法】首先利用三维激光雷达获取玉米植株点云数据,同时将相机采集的图像利用超绿化算法和最大类间方差法自动获得绿色特征二值图像,然后将聚类分析后的点云数据投影到图像中的目标边框上,构建多传感器数据融合支持度模型进行特征识别,最后拟合所获取特征中心点即为导航基准线。【结果】该算法能够很好地适应复杂环境,具有很强的抗干扰能力,单帧平均处理时间仅为95.62 ms,正确率高达95.33%。【结论】该算法解决了传统算法寻找特征质心偏移、识别结果不可靠等问题,为机器人在玉米田间行走提供了可靠的、实时的导航路径。

【Objective】In view of the fuzzy and irregular shape of the path boundary in the corn field,the common field navigation line extraction algorithm will have the problem of excessive deviation in practical application of agricultural robot.In this paper,a navigation line extraction algorithm based on discrete factor fusion of camera and 3D LiDAR is proposed for the field of corn at 3rd−5th leaf stage.【Method】First,three-dimensional lidar was used to obtain corn plant point cloud data.At the same time,the green feature binary images were obtained from the images taken by the camera using the super-green algorithm and the maximum between-cluster variance method,and then the point cloud data after cluster analysis were projected onto the target bounding box in the image.A multi-sensor data fusion support model was constructed for feature recognition.Finally the acquired feature center point was fitted as the navigation baseline.【Result】The algorithm could adapt well to complex environments and had strong anti-interference ability.The average processing time of a single frame was only 95.62 ms,and the accuracy rate was as high as 95.33%.【Conclusion】The algorithm solves the problems of shifting in finding feature centroid and unreliable recognition results in traditional algorithms,and provides a reliable and real-time navigation path for the robot to walk in corn field.