Modern agricultural applications require knowledge about  position and size of fruits on plants. However, occlusions from leaves typically make obtaining this information difficult. We present a novel viewpoint planning approach that generates an octree of plants which marks the regions of interests  , i.e., fruits. Our method uses this octree to sample viewpoints that increase the information around the fruit regions and estimates their position and size . Our planner is modular and provides different ways of obtaining the regions of interest and sampling candidate viewpoints . We evaluated our approach in simulated scenarios , where we compared the resulting fruit estimations with a known ground truth. The results demonstrate that our approach outperforms two different sampling methods that do not explicitly consider the regions of interest . Furthermore, we showed the real-world applicability by testing the framework on a robotic arm equipped with an RGB-D camera installed on an automated pipe-rail trolley in a capsicum glasshouse.
Modern agricultural applications require knowledge about the position and size of fruits on plants. However, occlusions from leaves typically make obtaining this information difficult. We present a novel viewpoint planning approach that builds up an octree of plants with labelled regions of interests (ROIs) , i.e., fruits. Our method uses this octree to sample viewpoint candidates that increase the information around the fruit regions and evaluates them using a heuristic utility function that takes into account the expected information gain. Our system automatically switches between ROI targeted sampling and exploration sampling, which considers general frontier voxels, depending on the estimated utility. When the plants have been sufficiently covered with the RGB-D sensor, our system clusters the ROI voxels and estimates the position and size of the detected fruits . We evaluated our approach in simulated scenarios and compared the resulting fruit estimations with the ground truth. The results demonstrate that our combined approach outperforms a sampling method that does not explicitly consider the ROIs to generate viewpoints in terms of the amount of discovered ROI cells . Furthermore, we show the real-world applicability by testing our framework on a robotic arm equipped with an RGB-D camera installed on an automated pipe-rail trolley in a capsicum glasshouse.
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