Foundation Feature-Driven Online End-Effector Pose Estimation: A Marker-Free and Learning-Free Approach

• URL: http://arxiv.org/abs/2503.14051v1
• Date: Tue, 18 Mar 2025 09:12:49 GMT
• Title: Foundation Feature-Driven Online End-Effector Pose Estimation: A Marker-Free and Learning-Free Approach
• Authors: Tianshu Wu, Jiyao Zhang, Shiqian Liang, Zhengxiao Han, Hao Dong,
• Abstract summary: This work proposes a feature-driven online End-Effect-or Pose Estimation algorithm.<n>It generalizes across robots and end-effectors in a training-free manner.<n>Experiments demonstrate its superior flexibility, generalization, and performance.
• Score: 4.132336580197184
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate transformation estimation between camera space and robot space is essential. Traditional methods using markers for hand-eye calibration require offline image collection, limiting their suitability for online self-calibration. Recent learning-based robot pose estimation methods, while advancing online calibration, struggle with cross-robot generalization and require the robot to be fully visible. This work proposes a Foundation feature-driven online End-Effector Pose Estimation (FEEPE) algorithm, characterized by its training-free and cross end-effector generalization capabilities. Inspired by the zero-shot generalization capabilities of foundation models, FEEPE leverages pre-trained visual features to estimate 2D-3D correspondences derived from the CAD model and target image, enabling 6D pose estimation via the PnP algorithm. To resolve ambiguities from partial observations and symmetry, a multi-historical key frame enhanced pose optimization algorithm is introduced, utilizing temporal information for improved accuracy. Compared to traditional hand-eye calibration, FEEPE enables marker-free online calibration. Unlike robot pose estimation, it generalizes across robots and end-effectors in a training-free manner. Extensive experiments demonstrate its superior flexibility, generalization, and performance.

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