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[Submitted on 13 Oct 2026] Title:Receding-Horizon Next-Best-View Planning for Highly Occluded Target Perception View a PDF of the paper titled Receding-Horizon Next-Best-View Planning for Highly Occluded Target Perception, by Ayse Gul Yildiz and 1 other authors View PDF HTML (experimental) Abstract: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. Active perception must therefore select viewpoints that progressively reveal unobserved regions. 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. 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. 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 63.8% ROI coverage versus 34.2% and 85.5% occluded-surface recall versus 7.4% for the single-step NBV baselines on the most occluded mug configuration. A controlled horizon ablation shows that this improvement arises from multi-step reasoning rather than additional computation. RH-NBV provides the greatest benefit for strongly occluded targets, while myopic NBV remains competitive and substantially cheaper when visibility is less constrained. Comments: 9 pages, 10 figures, 4 tables Subjects: Robotics (cs.RO) Cite as: arXiv:2610.19082 [cs.RO] (or arXiv:2610.19082v1 [cs.RO] for this version) https://doi.org/10.48550/arXiv.2610.19082 arXiv-issued DOI via DataCite (pending registration) Submission history From: Rekha Raja [view email] [v1] Tue, 13 Oct 2026 19:14:02 UTC (10,810 KB) Full-text links: Access Paper: View a PDF of the paper titled Receding-Horizon Next-Best-View Planning for Highly Occluded Target Perception, by Ayse Gul Yildiz and 1 other authors View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: cs.RO new | recent | 2026-10 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Loading... Data provided by: Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)