Vision-Based Camera/Robot Pose Estimation Using Both Semantic and Geometric Features on LEGO Baseplates
Vision-Based Camera/Robot Pose Estimation Using Both Semantic and Geometric Features on LEGO Baseplates
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DOI：
10.1109/case56687.2023.10260305
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发表时间：
2023-08
期刊：
影响因子：
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通讯作者：
Shu-Hao Yeh;Shuangyun Xie;Wei Yan;Dezhen Song
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文献类型：
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作者：
Shu-Hao Yeh;Shuangyun Xie;Wei Yan;Dezhen Song
我们正在探索在校准、导航或增强现实 (AR) 应用中使用乐高底板作为机器人和相机的人工地标 (AL) 的可能性。乐高底板坚固、用途广泛、成本低廉且制造精密，似乎是 AL 的绝佳候选者。然而，它们也是单色的，对比度低，容易受光照影响。为了克服这些问题，我们通过利用网格图案、圆形螺柱形状和文本图案，在算法设计中利用几何和语义信息。我们的算法使用鲁棒的估计方法广泛利用了噪声过滤和位置细化中的交叉验证信息。我们已经实施并成功测试了我们的算法。结果表明，我们的算法可以恢复超过 95% 的螺柱中心作为特征点，从而确保姿态估计的准确性。我们的实验还表明，当没有计算机视觉背景的用户部署这两种方法时，乐高底板比现有最先进的同类方法产生的相机姿态估计结果明显更准确。
We are exploring the possibility of using LEGO baseplate as artificial landmarks (ALs) for robots and cameras in calibration, navigation or Augment Reality (AR) applications. LEGO baseplates are rigid, widely-available, low-cost, and precisely-manufactured and appear to be great candidate for ALs. However, they are also monochromatic with low contrast and easily affected by lighting. To overcome those issues, we utilize geometric and semantic information in our algorithm design by leveraging grid pattern, circle stud shapes, and text patterns. Our algorithm has extensively utilized the information for cross validation in noise filtering and position refinement using robust estimation methods. We have implemented and successfully tested our algorithm. The results show that our algorithm can recover more than 95% stud centers as feature points which ensures pose estimation accuracy. Our experiments also show that LEGO baseplate produces significantly more accurate camera pose estimation results than that of existing state-of-the-art counterpart when both methods are deployed by users with no computer vision background.