A Pose Estimation Method for Manipulator Grasping in Unstructured Environments

• Jingyao Yan,
• Botao Zhang,
• Zhuang Liu,
• Chaohao Hua,
• Qiang Lu,

Vol. 19, No. 4, April 30, 2025
10.3837/tiis.2025.04.009, Download Paper (Free):

• Manipulator
• Pose Estimation
• Visual Servoing
• Point Pair Features

Abstract

In unstructured environments, it is crucial to quickly and accurately calculate the pose of the object before using a manipulator to grip it. Irrelevant point clouds will increase computation time and decrease the probability of successful grasps. In this study, a novel pose estimation approach for manipulator grasping based on Point Pair Features (PPF) and Speeded Up Robust Features (SURF) is presented. The pose estimation procedure consists of two phases: offline and online. 3D object models are employed in the offline stage to build the model database and the ideal grasping position database. The SURF and Random Sample Consensus (RANSAC) algorithms are utilized to detect and mark the target's region in RGB images during the online stage. Segmenting the target point clouds will reduce the influence of redundant point clouds on pose estimation. The pose is calculated by a voting approach, followed by the use of iterative closest point (ICP) and pose clustering to optimize the pose. The experimental grasping results showed that the proposed method significantly improves both the success rate of grasps and real-time performance.

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