Receding-Horizon Next-Best-View Planning for Highly Occluded Target Perception

Key points

• Robotic systems that must acquire information about a specific target often face severe occlusion from sur- rounding structures or from the target itself, such that a single camera viewpoint provides only partial information.1
• Active perception must therefore select viewpoints that progressively reveal unobserved regions.1
• However, conventional local next- best-view (NBV) methods typically select viewpoints according to their immediate utility, which can overlook views that provide limited information initially but enable more informative obser- vations from subsequent viewpoints.1
• RH-NBV is evaluated on a UR5e manipulator with an eye-in-hand RGB-D camera in simulation as well as on physical robot against a gradient-based NBV planner, and random search, using a coffee mug and Stanford bunny under controlled occlusion and a tomato plant with natural self-occlusion.1

Sources (1)

• [1]Receding-Horizon Next-Best-View Planning for Highly Occluded Target Perception
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 13, 07:14 PM

  We formulated local target perception as a short-horizon sequential view-planning problem and proposed a Receding-Horizon Next-Best-View (RH-NBV) planner that evaluates candidate sequences of future viewpoints rather than isolated views.

  Robotic systems that must acquire information about a specific target often face severe occlusion from sur- rounding structures or from the target itself, such that a single camera viewpoint provides only partial information.

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