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Updated: Feb 17, 2026

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

Tie Ye1, Zhuo Wang1, Yong Qin2

  • 1Intelligent Manufacturing Research Institute, Nanyang Normal University, Nanyang, China.

Annals of the New York Academy of Sciences
|February 15, 2026
PubMed
Summary

This study introduces a novel hypergraph framework for accurate autonomous forklift pallet pose estimation. It enhances reliability in challenging warehouse conditions like occlusion and poor lighting.

Keywords:
hypergraph computingkeypoint detectionknowledge‐enhanced learningpallet pose estimationwarehouse automation

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Area of Science:

  • Computer Vision
  • Robotics
  • Artificial Intelligence

Background:

  • Autonomous forklift operations are hindered by challenges like dense stacking, occlusions, and variable lighting, impacting positioning accuracy.
  • Efficient material handling in warehouses requires robust and precise pallet localization.

Purpose of the Study:

  • To develop a knowledge-enhanced framework using hypergraph computing for accurate pallet pose estimation in complex industrial settings.
  • To improve the robustness and efficiency of autonomous forklift operations.

Main Methods:

  • Utilized hypergraph structures to model high-order spatial relationships among pallet keypoints, capturing geometric dependencies.
  • Integrated hypergraph computing with domain knowledge, employing a hyper-pose architecture with attention fusion and geometry-aware keypoint detection.
  • Implemented uncertainty-constrained pose estimation using Mahalanobis distance optimization.

Main Results:

  • Achieved high accuracy with 97.5% detection and 97.3% keypoint localization precision, while reducing model size to 20.2 MB via topology-preserving pruning.
  • Demonstrated real-time performance at 72.1 FPS on an edge computing platform (NVIDIA Jetson Orin Nano).
  • Obtained low error rates: <1.6° angular error and <18 mm distance error.

Conclusions:

  • The hypergraph computing and knowledge-enhanced framework offers a robust solution for pallet pose estimation in challenging warehouse environments.
  • This approach significantly enhances the reliability of automated material handling systems.
  • The study establishes a new paradigm for industrial vision systems, improving autonomous operations.