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<p>Accurate estimation of a rigid object’s 6D pose and twist is a fundamental challenge for enabling autonomous systems operation and interaction with the environment. Monocular vision can mitigate the drift inherent in inertial methods and enables the estimation of non-cooperative target twist. However, the estimation accuracy of monocular vision is compromised by inherent depth ambiguity, a limitation that multi-camera systems can overcome. The lack of multi-view datasets with high-precision annotations hinders the development of robust perception algorithms. To address this, we present the first trinocular pose and twist estimation dataset for non-cooperative targets, comprising images of aircraft (7,824 for training, 4,710 for testing) and satellites (7683 for training, 4380 for testing), all manually annotated with sub-pixel-level keypoints and pose labels derived from optimization. We develop a neural network that predicts semantic keypoints for robust pose estimation. Combined with a multi-view optimization framework and twist estimation, our system achieves a mean angular velocity error of <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(0.1^{\circ }\)</EquationSource> </InlineEquation>/s and a mean linear velocity error of 0.3mm/s. Our open-source dataset and method provide a critical benchmark for future research in aerospace missions. We have open-sourced the dataset at the following <a href="https://www.kaggle.com/datasets/mingshiwuwjh/trinocular-pose-and-twist-estimation-dataset.">https://www.kaggle.com/datasets/mingshiwuwjh/trinocular-pose-and-twist-estimation-dataset.</a></p>

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Trinocular vision with deep learning for object twist estimation: a benchmark approach and dataset

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月8日

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  • Jinghao Wang,
  • KaiKai Cui,
  • Teng Teng,
  • Zi Wang,
  • Boming Ren,
  • Qifeng Yu

摘要

Accurate estimation of a rigid object’s 6D pose and twist is a fundamental challenge for enabling autonomous systems operation and interaction with the environment. Monocular vision can mitigate the drift inherent in inertial methods and enables the estimation of non-cooperative target twist. However, the estimation accuracy of monocular vision is compromised by inherent depth ambiguity, a limitation that multi-camera systems can overcome. The lack of multi-view datasets with high-precision annotations hinders the development of robust perception algorithms. To address this, we present the first trinocular pose and twist estimation dataset for non-cooperative targets, comprising images of aircraft (7,824 for training, 4,710 for testing) and satellites (7683 for training, 4380 for testing), all manually annotated with sub-pixel-level keypoints and pose labels derived from optimization. We develop a neural network that predicts semantic keypoints for robust pose estimation. Combined with a multi-view optimization framework and twist estimation, our system achieves a mean angular velocity error of \(0.1^{\circ }\) /s and a mean linear velocity error of 0.3mm/s. Our open-source dataset and method provide a critical benchmark for future research in aerospace missions. We have open-sourced the dataset at the following https://www.kaggle.com/datasets/mingshiwuwjh/trinocular-pose-and-twist-estimation-dataset.