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Inspired by recent promising results in sim-to-real transfer in deep learningwe built a realistic simulation environment combining a Robot Operating System(ROS)-compatible physics simulator (Gazebo) with Cycles, the realisticproduction rendering engine from Blender. The proposed simulator pipelineallows us to simulate near-realistic RGB-D images. To showcase the capabilitiesof the simulator pipeline we propose a case study that focuses on indoorrobotic farming. We developed a solution for sweet pepper yield estimationtask. Our approach to yield estimation starts with aerial robotics control andtrajectory planning, combined with deep learning-based pepper detection, and aclustering approach for counting fruit. The results of this case study showthat we can combine real time dynamic simulation with near realistic renderingcapabilities to simulate complex robotic systems.

Render-in-the-loop aerial robotics simulator: Case Study on Yield Estimation in Indoor Agriculture