论文标题
渲染循环空中机器人模拟器：室内农业中产量估计的案例研究
Render-in-the-loop aerial robotics simulator: Case Study on Yield Estimation in Indoor Agriculture
论文作者
论文摘要
受到深度学习中的SIM到现实转移的最新有希望的结果的启发，我们构建了一个现实的模拟环境，结合了机器人操作系统（ROS）兼容物理模拟器（Gazebo）和Cycles和Cycles，这是Blender的逼真的生产渲染引擎。提出的模拟器管道使我们能够模拟近现代的RGB-D图像。为了展示模拟器管道的功能，我们提出了一项案例研究，重点是室内机器人农业。我们开发了一种用于甜椒产量估计任务的解决方案。我们的产量估计方法始于空中机器人技术控制和轨迹计划，再加上基于深度学习的胡椒检测，以及用于计算水果的聚类方法。该案例研究的结果表明，我们可以将实时动态模拟与几乎逼真的渲染能力结合起来，以模拟复杂的机器人系统。
Inspired by recent promising results in sim-to-real transfer in deep learning we built a realistic simulation environment combining a Robot Operating System (ROS)-compatible physics simulator (Gazebo) with Cycles, the realistic production rendering engine from Blender. The proposed simulator pipeline allows us to simulate near-realistic RGB-D images. To showcase the capabilities of the simulator pipeline we propose a case study that focuses on indoor robotic farming. We developed a solution for sweet pepper yield estimation task. Our approach to yield estimation starts with aerial robotics control and trajectory planning, combined with deep learning-based pepper detection, and a clustering approach for counting fruit. The results of this case study show that we can combine real time dynamic simulation with near realistic rendering capabilities to simulate complex robotic systems.
