Performing tasks in agriculture, such as fruit monitoring or harvesting, requires perceiving the objects' spatial position. RGB-D cameras are limited under open-field environments due to lightning interferences. So, in this study, we state to answer the research question: "How can we use and control monocular sensors to perceive objects' position in the 3D task space?" Towards this aim, we approached histogram filters (Bayesian discrete filters) to estimate the position of tomatoes in the tomato plant through the algorithm MonoVisual3DFilter. Two kernel filters were studied: the square kernel and the Gaussian kernel. The implemented algorithm was essayed in simulation, with and without Gaussian noise and random noise, and in a testbed at laboratory conditions. The algorithm reported a mean absolute error lower than 10 mm in simulation and 20 mm in the testbed at laboratory conditions with an assessing distance of about 0.5 m. So, the results are viable for real environments and should be improved at closer distances.

翻译：在农业中执行果实监测或收获等任务，需要感知物体的空间位置。RGB-D相机在开放田间环境下因光照干扰而受限。因此，本研究旨在回答以下研究问题：“如何利用并控制单目传感器来感知三维任务空间中物体的位置？”为此，我们采用直方图滤波器（贝叶斯离散滤波器），通过MonoVisual3DFilter算法估算番茄植株上番茄的位置。研究了两种核滤波器：方形核与高斯核。所实现的算法在仿真环境（添加与不添加高斯噪声及随机噪声）以及实验室条件下的测试平台中进行了验证。该算法在仿真中的平均绝对误差低于10毫米，在评估距离约0.5米的实验室测试平台中平均绝对误差低于20毫米。因此，该结果适用于真实环境，并应在更近距离下进一步优化。