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<p>Real-time posture estimation based on incomplete three-dimensional (3D) measurements is crucial for vision systems used in industrial robots. Conventional systems rely on manual pre-registering of multiple partial point cloud models for each workpiece and often fail when the 3D sensor is repositioned or its viewpoint changes. To overcome this bottleneck, we extend a fast MaskNet + singular‑value‑decomposition framework. However, its time‑series estimates still fluctuate owing to sensor and inference noise. To improve accuracy under realistic conditions, MaskNet was retrained on a large ray‑casting‑augmented CAD dataset that simulates random sensor viewpoints, and a Kalman filter was introduced to suppress temporal noise. The new training enhances mask‑vector accuracy, and the Kalman filter suppresses temporal fluctuations. Experiments confirm that the proposed method operates in real time on standard hardware, requires no pre‑registration after sensor movement, and can be seamlessly incorporated into a robot vision system for reliable target‑picking tasks.</p>

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Development of a MaskNet-based posture estimation method for robot vision systems

  • 期刊论文

  • 发表日期：

    2025年7月6日

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  • Yu Iwai,
  • Soma Fumoto,
  • Masato Kitamura,
  • Takeshi Nishida

摘要

Real-time posture estimation based on incomplete three-dimensional (3D) measurements is crucial for vision systems used in industrial robots. Conventional systems rely on manual pre-registering of multiple partial point cloud models for each workpiece and often fail when the 3D sensor is repositioned or its viewpoint changes. To overcome this bottleneck, we extend a fast MaskNet + singular‑value‑decomposition framework. However, its time‑series estimates still fluctuate owing to sensor and inference noise. To improve accuracy under realistic conditions, MaskNet was retrained on a large ray‑casting‑augmented CAD dataset that simulates random sensor viewpoints, and a Kalman filter was introduced to suppress temporal noise. The new training enhances mask‑vector accuracy, and the Kalman filter suppresses temporal fluctuations. Experiments confirm that the proposed method operates in real time on standard hardware, requires no pre‑registration after sensor movement, and can be seamlessly incorporated into a robot vision system for reliable target‑picking tasks.