mlsanityData to 14 Oct 2026

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

arXiv 2503.14051 ↗First posted 18 Mar 2025cs.RO cs.CVcited by 1

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 42 of its 56 references that are in the map. First posted on arXiv 18 Mar 2025.

Builds on

42 of 56 references.

References · 42

  1. 2023Segment Anythingcited by 8,234
  2. 2023DINOv2: Learning Robust Visual Features without Supervisioncited by 7,974
  3. 2017PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenescited by 775
  4. 2023FoundationPose: Unified 6D Pose Estimation and Tracking of Novel Objectscited by 429
  5. 2019DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusioncited by 355
  6. 2021Deep ViT Features as Dense Visual Descriptorscited by 330
  7. 2023A Tale of Two Features: Stable Diffusion Complements DINO for Zero-Shot Semantic Correspondencecited by 301
  8. 2020CosyPose: Consistent multi-view multi-object 6D pose estimationcited by 287
  9. 2023Track Anything: Segment Anything Meets Videoscited by 234
  10. 2019Pix2Pose: Pixel-Wise Coordinate Regression of Objects for 6D Pose Estimationcited by 218
  11. 2019PVN3D: A Deep Point-wise 3D Keypoints Voting Network for 6DoF Pose Estimationcited by 204
  12. 2022MegaPose: 6D Pose Estimation of Novel Objects via Render & Comparecited by 201
30 more
  1. 2021FFB6D: A Full Flow Bidirectional Fusion Network for 6D Pose Estimationcited by 144
  2. 2023SAM-6D: Segment Anything Model Meets Zero-Shot 6D Object Pose Estimationcited by 117
  3. 2023GigaPose: Fast and Robust Novel Object Pose Estimation via One Correspondencecited by 114
  4. 2023FoundPose: Unseen Object Pose Estimation with Foundation Featurescited by 109
  5. 2021BundleTrack: 6D Pose Tracking for Novel Objects without Instance or Category-Level 3D Modelscited by 87
  6. 2022OSOP: A Multi-Stage One Shot Object Pose Estimation Frameworkcited by 81
  7. 2025OmniManip: Towards General Robotic Manipulation via Object-Centric Interaction Primitives as Spatial Constraintscited by 59
  8. 2022Templates for 3D Object Pose Estimation Revisited: Generalization to New Objects and Robustness to Occlusionscited by 54
  9. 2022Domain Randomization-Enhanced Depth Simulation and Restoration for Perceiving and Grasping Specular and Transparent Objectscited by 50
  10. 2024Omni6DPose: A Benchmark and Model for Universal 6D Object Pose Estimation and Trackingcited by 50
  11. 2019Camera-to-Robot Pose Estimation from a Single Imagecited by 48
  12. 2023GenPose: Generative Category-level Object Pose Estimation via Diffusion Modelscited by 48
  13. 2024Graspness Discovery in Clutters for Fast and Accurate Grasp Detectioncited by 48
  14. 2023ZS6D: Zero-shot 6D Object Pose Estimation using Vision Transformerscited by 45
  15. 2021Single-view robot pose and joint angle estimation via render & comparecited by 33
  16. 2024GenFlow: Generalizable Recurrent Flow for 6D Pose Refinement of Novel Objectscited by 33
  17. 2024MatchU: Matching Unseen Objects for 6D Pose Estimation from RGB-D Imagescited by 28
  18. 2023Markerless Camera-to-Robot Pose Estimation via Self-supervised Sim-to-Real Transfercited by 24
  19. 2023GraspGF: Learning Score-based Grasping Primitive for Human-assisting Dexterous Graspingcited by 23
  20. 2023EasyHeC: Accurate and Automatic Hand-eye Calibration via Differentiable Rendering and Space Explorationcited by 17
  21. 2020Pose Estimation for Robot Manipulators via Keypoint Optimization and Sim-to-Real Transfercited by 16
  22. 2023RGBGrasp: Image-based Object Grasping by Capturing Multiple Views during Robot Arm Movement with Neural Radiance Fieldscited by 13
  23. 2022Unseen Object 6D Pose Estimation: A Benchmark and Baselinescited by 12
  24. 2024Object Pose Estimation via the Aggregation of Diffusion Featurescited by 12
  25. 2024Canonical Representation and Force-Based Pretraining of 3D Tactile for Dexterous Visuo-Tactile Policy Learningcited by 11
  26. 2024RoboKeyGen: Robot Pose and Joint Angles Estimation via Diffusion-based 3D Keypoint Generationcited by 10
  27. 2023Robot Structure Prior Guided Temporal Attention for Camera-to-Robot Pose Estimation from Image Sequencecited by 9
  28. 2023KeyMatchNet: Zero-Shot Pose Estimation in 3D Point Clouds by Generalized Keypoint Matchingcited by 7
  29. 2023LVDiffusor: Distilling Functional Rearrangement Priors from Large Models into Diffusorcited by 7
  30. 2022Learning Markerless Robot-Depth Camera Calibration and End-Effector Pose Estimationcited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026RoboWM-Bench: A Benchmark for Evaluating World Models in Robotic Manipulationcited by 19