Non-Penetration Iterative Closest Points for Single-View Multi-Object 6D Pose Estimation
Published in IEEE International Conference on Robotics and Automation (ICRA), 2022
Abstract
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.
Citation
@inproceedings{zhang2022non,
title={Non-Penetration Iterative Closest Points for Single-View Multi-Object 6D Pose Estimation},
author={Zhang, Mengchao and Hauser, Kris},
booktitle={2022 International Conference on Robotics and Automation (ICRA)},
pages={1520–1526},
year={2022},
organization={IEEE}
}