Differentiable Rendering-based Pose Estimation for Surgical Robotic Instruments

2025-03-07

AI Summary

Accurate pose initialization for cable-driven surgical robots (e.g., the da Vinci system) is hindered by joint angle measurement errors and partial observability of the kinematic chain, especially in markerless settings. Method: This paper proposes a single-shot, markerless calibration method that integrates differentiable rendering with cylindrical geometric priors to construct a structure-aware pose hypothesis space—bypassing the conventional keypoint detection + SolvePnP pipeline—and establishes an end-to-end differentiable pose matching framework. Joint optimization of pose and shape parameters is achieved via gradient-based backpropagation through rendered image gradients. Contribution/Results: The method significantly improves initialization robustness and generalization across unseen configurations. Experiments demonstrate state-of-the-art performance in both calibration consistency and real surgical scenarios, enabling markerless, real-time, high-precision instrument pose estimation.

Technology Category

• Intelligent Robots: State Estimation
• Computer Vision: Biometrics, Face, Gesture & Pose
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

Robot pose estimation is a challenging and crucial task for vision-based surgical robotic automation. Typical robotic calibration approaches, however, are not applicable to surgical robots, such as the da Vinci Research Kit (dVRK), due to joint angle measurement errors from cable-drives and the partially visible kinematic chain. Hence, previous works in surgical robotic automation used tracking algorithms to estimate the pose of the surgical tool in real-time and compensate for the joint angle errors. However, a big limitation of these previous tracking works is the initialization step which relied on only keypoints and SolvePnP. In this work, we fully explore the potential of geometric primitives beyond just keypoints with differentiable rendering, cylinders, and construct a versatile pose matching pipeline in a novel pose hypothesis space. We demonstrate the state-of-the-art performance of our single-shot calibration method with both calibration consistency and real surgical tasks. As a result, this marker-less calibration approach proves to be a robust and generalizable initialization step for surgical tool tracking.

Problem

• Estimating surgical robot pose accurately
• Overcoming joint angle measurement errors
• Improving initialization for surgical tool tracking

Innovation

• Differentiable rendering for pose estimation
• Cylinders used in pose matching pipeline
• Marker-less calibration for surgical tools