Accurate soil mapping is critical for a highly-automated agricultural vehicle to successfully accomplish important tasks including seeding, ploughing, fertilising and controlled traffic, with limited human supervision, ensuring at the same time high safety standards. In this research, a multi-sensor ground mapping and characterisation approach is proposed, whereby data coming from heterogeneous but complementary sensors, mounted on-board an unmanned rover, are combined to generate a multi-layer map of the environment and specifically of the supporting ground. The sensor suite comprises both exteroceptive and proprioceptive devices. Exteroceptive sensors include a stereo camera, a visible and near infrared camera and a thermal imager. Proprioceptive data consist of the vertical acceleration of the vehicle sprung mass as acquired by an inertial measurement unit. The paper details the steps for the integration of the different sensor data into a unique multi-layer map and discusses a set of exteroceptive and proprioceptive features for soil characterisation and change detection. Experimental results obtained with an all-terrain vehicle operating on different ground surfaces are presented. It is shown that the proposed technologies could be potentially used to develop all-terrain self-driving systems in agriculture. In addition, multi-modal soil maps could be useful to feed farm management systems that would present to the user various soil layers incorporating colour, geometric, spectral and mechanical properties.
翻译:精确的土壤测绘对于高度自动化的农用车辆成功完成重要任务至关重要,包括播种、犁耕、施肥和控制交通,以及有限的人监督,确保高安全标准。在这一研究中,提出了多传感器地面测绘和特征描述方法,将安装在无人驾驶路虎上的多元但互补的传感器数据组合在一起,以产生一套多层环境图,特别是辅助地面图。传感器套件包括外向感应和自主感应装置。外向感应传感器包括立体相机、可见的近红外摄像仪和热成像仪。优先感应数据包括由惯性测量单元获得的车辆脉冲质量垂直加速。文件详细介绍了将不同传感器数据纳入独特的多层图的步骤,并讨论了一套用于土壤定性和变化探测的外向感应和偏感特性。通过在不同地面表面运行的全地形飞行器获得的实验结果。在提出各种地形图时,可以使用各种应用的系统来进行自我调节。在地面系统上,可以使用各种应用的系统进行多轨化技术来进行自我调节。