用几何矩对齐物体对称性,提升抓取稳定性。
Global Symmetry and Orthogonal Transformations from Geometrical Moment $n$-tuples
- 基于几何矩的n元组分析物体对称性与正交变换
- 在2D/3D物体上验证,可高效识别多个对称面
- 适合机器人抓取与三维形状分析场景
对称性检测对有效物体抓取至关重要。识别物体内的对称特征或轴线有助于制定高效抓取策略,沿对称轴抓取通常能获得更稳定平衡的握持,从而提升操作成功率。本文利用几何矩识别对称性并估计物体中心位于坐标原点时的正交变换(包括旋转和镜像变换),提出独特的度量方法以检测对称性并估计旋转、反射及其组合。开发了适用于n维空间的完整方法论,具体为矩n元组。在2D和3D物体上进行了大量验证测试,以确保方法的鲁棒性和可靠性。将该方法与基于迭代优化的最新方法对比,结果显示:将本方法与迭代法结合,在对称面数量检测和计算时间方面均取得满意结果。
原文摘要 · Abstract (English)
Detecting symmetry is crucial for effective object grasping for several reasons. Recognizing symmetrical features or axes within an object helps in developing efficient grasp strategies, as grasping along these axes typically results in a more stable and balanced grip, thereby facilitating successful manipulation. This paper employs geometrical moments to identify symmetries and estimate orthogonal transformations, including rotations and mirror transformations, for objects centered at the frame origin. It provides distinctive metrics for detecting symmetries and estimating orthogonal transformations, encompassing rotations, reflections, and their combinations. A comprehensive methodology is developed to obtain these functions in n-dimensional space, specifically moment \( n \)-tuples. Extensive validation tests are conducted on both 2D and 3D objects to ensure the robustness and reliability of the proposed approach. The proposed method is also compared to state-of-the-art work using iterative optimization for detecting multiple planes of symmetry. The results indicate that combining our method with the iterative one yields satisfactory outcomes in terms of the number of symmetry planes detected and computation time.
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