Non-Penetration Iterative Closest Points for Single-View Multi-Object 6D Pose Estimation
Non-Penetration Iterative Closest Points for Single-View Multi-Object 6D Pose Estimation
复制标题
DOI：
10.1109/icra46639.2022.9812043
复制
发表时间：
2022-05
期刊：
影响因子：
--
通讯作者：
Mengchao Zhang;Kris K. Hauser
中科院分区：
文献类型：
--
作者：
Mengchao Zhang;Kris K. Hauser
提出了一种新的迭代最近点算法，即非穿透迭代最近点算法，用于防止六自由度位姿优化和/或多目标位姿联合优化中的穿透问题。此功能在杂乱的场景中特别有用，在这种场景中，对象之间存在许多交互，限制了有效姿势的空间。我们使用半无限规划方法来处理复杂的、非凸的3D几何之间的非穿透约束。为了提高粗略猜测的位姿估计精度，将NPICP作为一种后处理方法应用于一种常见的ICP用例。结果表明，在6维目标位姿估计的基准中，NPICP的性能优于ICP，有助于孤立点的检测，也优于IC-BIN数据集上的最佳结果。
This paper presents a novel iterative closest points (ICP) variant, non-penetration iterative closest points (NPICP), which prevents interpenetration in 6DOF pose optimization and/or joint optimization of multiple object poses. This capability is particularly advantageous in cluttered scenarios, where there are many interactions between objects that constrain the space of valid poses. We use a semi-infinite programming approach to handle non-penetration constraints between complex, non-convex 3D geometries. NPICP is applied to a common use case for ICP as a post-processing method to improve the pose estimation accuracy of a rough guess. The results show that NPICP outperforms ICP, assists in outlier detection, and also outperforms the best result on the IC-BIN dataset in the Benchmark for 6D Object Pose Estimation.