Receding-Horizon Next-Best-View Planning for Highly Occluded
Overview
Source roundup from published reports. Claims below are attributed to their publishers, not independently verified. arXiv Robotics — research abstracts: Receding-Horizon Next-Best-View Planning for Highly Occluded Target Perception. This research addresses the challenge of robotic perception in highly occluded environments by proposing a receding-horizon next-best-view (RH-NBV) planner. The method evaluates sequences of future viewpoints to progress…
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Event evidence and corrections
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Environment: simulation
“nts rather than isolated views. 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. Under heavy occlusion, RH-NBV achieves substantially higher target information recovery, reaching 63”
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- arXiv Robotics — research abstractsSchr"oding, Navigator: Imagining an Ensemble of Futures for Zero-Shot Object Navigation
This research proposes Schr"odinger's Navigator, a belief-aware framework for zero-shot object navigation that reasons over multiple trajectory-conditioned imagined 3D futures. The method improves hidden-target discovery and risk-aware waypoint selection in occluded environments through a Future-Aware Value Map and adaptive occluder-aware sampling, outperforming existing baselines in both simulation and physical testing.
- arXiv Robotics — research abstractsReceding-Horizon Next-Best-View Planning for Highly Occluded Target Perception
This research addresses the challenge of robotic perception in highly occluded environments by proposing a receding-horizon next-best-view (RH-NBV) planner. The method evaluates sequences of future viewpoints to progressively reveal unobserved regions of a target. Evaluated on both simulation and physical robots, RH-NBV achieves significantly higher target information recovery under heavy occlusion compared to conventional NBV approaches, with performance improvements attributed to multi-step reasoning.
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