Depth-Integrated Spatial Mapping for Enhanced Robotic Placement Accuracy

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Published: 2026-01-20
Keywords:
spatial mapping, depth reconstruction, industrial placement, geometry optimization, robotic assembly

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Daniel A. Levin
School of Mechanical Engineering, Tel Aviv University, Tel Aviv, 6997801, Israel
Maya R. Shapira
School of Mechanical Engineering, Tel Aviv University, Tel Aviv, 6997801, Israel
Eitan Goldfarb
School of Mechanical Engineering, Tel Aviv University, Tel Aviv, 6997801, Israel

Abstract

Accurate robotic placement of small industrial components requires stable depth interpretation under noisy factory conditions. We present a depth-integrated spatial mapping framework that reconstructs volumetric geometric fields using denoised depth cues and surface continuity priors. A geometry-consistency optimizer corrects depth-induced inconsistencies, enhancing spatial reliability. Tests on AssemblyDepth-2025 and RoboPlacement datasets show reductions of 29.1% in spatial variance and 17.4% in depth distortion. Real-factory deployment improves placement accuracy by 22.5% and reduces misalignment events by 19.1%. The framework demonstrates strong repeatability over 10,000+ cycles.

Article Details

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Vol. 1 No. 2 (2025): 2025 International Conference on Computer Science, Artificial Intelligence and Communication Systems (CSACS 2025)

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Articles

How to Cite

Depth-Integrated Spatial Mapping for Enhanced Robotic Placement Accuracy. (2026). Journal of Science, Innovation & Social Impact, 1(2), 168-174. https://sagespress.com/index.php/JSISI/article/view/71

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