阿拉伯农业的视觉-内皮农业测量系统实验评价 (Experimental Evaluation of Visual-Inertial Odometry Systems for Arable Farming)

The farming industry constantly seeks the automation of different processes involved in agricultural production, such as sowing, harvesting and weed control. The use of mobile autonomous robots to perform those tasks is of great interest. Arable lands present hard challenges for Simultaneous Localization and Mapping (SLAM) systems, key for mobile robotics, given the visual difficulty due to the highly repetitive scene and the crop leaves movement caused by the wind. In recent years, several Visual-Inertial Odometry (VIO) and SLAM systems have been developed. They have proved to be robust and capable of achieving high accuracy in indoor and outdoor urban environments. However, they were not properly assessed in agricultural fields. In this work we assess the most relevant state-of-the-art VIO systems in terms of accuracy and processing time on arable lands in order to better understand how they behave on these environments. In particular, the evaluation is carried out on a collection of sensor data recorded by our wheeled robot in a soybean field, which was publicly released as the Rosario Dataset. The evaluation shows that the highly repetitive appearance of the environment, the strong vibration produced by the rough terrain and the movement of the leaves caused by the wind, expose the limitations of the current state-of-the-art VIO and SLAM systems. We analyze the systems failures and highlight the observed drawbacks, including initialization failures, tracking loss and sensitivity to IMU saturation. Finally, we conclude that even though certain systems like ORB-SLAM3 and S-MSCKF show good results with respect to others, more improvements should be done to make them reliable in agricultural fields for certain applications such as soil tillage of crop rows and pesticide spraying.

翻译：农业产业不断寻求农业生产中不同过程的自动化,例如播种、收割和杂草控制。使用移动自主机器人来完成这些任务非常令人感兴趣。阿拉伯土地对同步本地化和绘图(SLAM)系统(流动机器人的关键,即流动机器人系统)提出了艰巨的挑战,因为由于风造成的高度重复和作物叶运动,在视觉上存在困难。近年来,已经开发了几套视觉化综合测量(VIO)和SLAM系统,这些系统在室内和室外城市环境中都证明是稳健的,能够实现高精度。然而,在农业领域,它们没有得到适当的评估。在这项工作中,我们从准确性和处理可耕地上最相关的最新甚高水平的VIO系统(SAM),以便更好地了解这些系统在这些环境中的运行情况。特别是,对由我们轮式机器人在豆类地区所记录的传感器数据的收集工作进行了评估,这些系统在罗萨里奥数据集中公开发布。评估表明,环境的高度重复性外表征,甚至对农业环境的敏感度进行了适当度评估。我们所观察到的SLAM3,从粗地和SLMS-RMS-RMS-RMS-R-R-L-R-R-L-L-L-L-L-L-L-R-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-