Learning-based Keypoints Detection with Topological Order on Deformable Linear Objects from Incomplete Point Clouds

Abstract

Detection of deformable linear objects (DLOs) in three-dimensional space is essential for robotic manipulation of DLOs. However, their complex deformations and high degrees of freedom make perception highly susceptible to occlusions, noise, and data missing. To address these challenges, we propose a deep learning-based method that leverages the topological properties of DLOs to robustly detect keypoints from incomplete point clouds while preserving the topological order of keypoints. Our approach initializes a sequence of keypoints that adheres to the topological structure of DLOs. Then, these ordered keypoints are refined through bidirectional sequence learning. Simulation results demonstrate that our method generates accurate, uniform, and smooth keypoint sequences under varying levels of occlusion. Compared to existing baselines, our approach achieves superior performance. Real-world experiments further validate the generalization capability of our method in unseen and challenging scenarios involving occlusion and self-occlusion while maintaining real-time performance.