A Hypergraph Computing and Knowledge-Enhanced Framework for Forklift Pallet Pose Estimation

Autonomous forklift operations in modern warehouses face critical challenges from dense pallet stacking, partial occlusions, and variable illumination conditions, which significantly impact positioning accuracy and operational efficiency. This paper presents a novel hypergraph computing and knowledge-enhanced framework for robust pallet pose estimation, addressing these complex industrial scenarios. The framework leverages hypergraph structures to model high-order spatial relationships among pallet keypoints, effectively representing geometric dependencies in E-shaped pallet configurations even under severe occlusion and stacking artifacts. By integrating hypergraph computing with domain knowledge, we develop a hyper-pose architecture that combines hierarchical attention fusion mechanisms with geometry-aware keypoint detection modules, encoding pallet structural priors as differentiable constraints. A topology-preserving pruning strategy specifically designed for hypergraph structures reduces the model to 20.2 MB, while maintaining 97.5% detection accuracy and 97.3% keypoint localization precision. The framework incorporates uncertainty constrained pose estimation using Mahalanobis distance optimization, achieving angular errors below 1.6° and distance errors under 18 mm. The system demonstrates real-time performance at 72.1 FPS on the NVIDIA Jetson Orin Nano edge computing platform. Extensive validation using 16,233 warehouse images confirms robust performance across challenging conditions. The integration of hypergraph computing with knowledge enhancement establishes a new paradigm for industrial vision systems, significantly improving automated material handling reliability.