Gradient-based Local Next-best-view Planning for Improved Perception of
  Targeted Plant Nodes
Akshay K. Burusa Eldert J. van Henten Gert Kootstra
Robots are increasingly used in tomato greenhouses to automate
labour-intensive tasks such as selective harvesting and de-leafing. To perform
these tasks, robots must be able to accurately and efficiently perceive the
plant nodes that need to be cut, despite the high levels of occlusion from
other plant parts. We formulate this problem as a local next-best-view (NBV)
planning task where the robot has to plan an efficient set of camera viewpoints
to overcome occlusion and improve the quality of perception. Our formulation
focuses on quickly improving the perception accuracy of a single target node to
maximise its chances of being cut. Previous methods of NBV planning mostly
focused on global view planning and used random sampling of candidate
viewpoints for exploration, which could suffer from high computational costs,
ineffective view selection due to poor candidates, or non-smooth trajectories
due to inefficient sampling. We propose a gradient-based NBV planner using
differential ray sampling, which directly estimates the local gradient
direction for viewpoint planning to overcome occlusion and improve perception.
Through simulation experiments, we showed that our planner can handle
occlusions and improve the 3D reconstruction and position estimation of nodes
equally well as a sampling-based NBV planner, while taking ten times less
computation and generating 28% more efficient trajectories.