Efficient Surgical Robotic Instrument Pose Reconstruction in Real World Conditions Using Unified Feature Detection

Abstract: Accurate camera-to-robot calibration is essential for any vision-based robotic control system and especially critical in minimally invasive surgical robots, where instruments conduct precise micro-manipulations. However, MIS robots have long kinematic chains and partial visibility of their degrees of freedom in the camera, which introduces challenges for conventional camera-to-robot calibration methods that assume stiff robots with good visibility. Previous works have investigated both keypoint-based and rendering-based approaches to address this challenge in real-world conditions; however, they often struggle with consistent feature detection or have long inference times, neither of which are ideal for online robot control. In this work, we propose a novel framework that unifies the detection of geometric primitives (keypoints and shaft edges) through a shared encoding, enabling efficient pose estimation via projection geometry. This architecture detects both keypoints and edges in a single inference and is trained on large-scale synthetic data with projective labeling. This method is evaluated across both feature detection and pose estimation, with qualitative and quantitative results demonstrating fast performance and state-of-the-art accuracy in challenging surgical environments.

• The paper presents a unified detection network that integrates keypoint and edge detection to enhance pose reconstruction accuracy.
• It leverages synthetic data and adaptive loss functions to achieve state-of-the-art performance in real-world MIS environments.
• Experimental results demonstrate significant improvements in inference speed and robustness, outperforming traditional PnP methods.

Efficient Surgical Robotic Instrument Pose Reconstruction in Real World Conditions Using Unified Feature Detection

Introduction

The paper introduces a novel framework for surgical robotic instrument pose reconstruction, aimed at addressing real-world challenges associated with visual-based robotic control in minimally invasive surgical (MIS) settings. Accurate camera-to-robot calibration is crucial in these scenarios due to the precise micro-manipulations that surgical instruments perform. Existing methods for pose estimation either suffer from consistency issues in feature detection or inefficient inference times, which are inadequate for real-time robotic control. This research proposes a unified approach that integrates keypoint and edge detection within a shared encoding framework, leveraging large-scale synthetic data and projective labeling for efficient surgical instrument pose estimation.

Unified Feature Detection

A significant contribution of the paper is the development of a unified detection network that simultaneously detects geometric primitives, such as keypoints and shaft edges, through a single inference. This integration is achieved with a shared backbone network that outputs refined spatial representations for both keypoints and line features. The model utilizes a DINOv2-L Vision Transformer architecture for feature extraction, offering strong cross-domain generalization capabilities. The Edge Net and Keypoint Net work collaboratively to project features into Hough space and pixel space, respectively, enhancing detection accuracy and robustness in complex surgical scenes.

Methodology

The framework uses synthetic data generated from high-resolution rendering engines to train the detection network, ensuring consistent and precise labeling without manual annotation. The training process involves adaptive loss functions like the Adaptive Wing Loss, optimizing network predictions on heatmaps. The feature-to-pose inference pipeline exploits geometric constraints and projective labeling to deliver real-time pose estimation. This process addresses the limitations of prior iterative optimization methods by providing direct geometric solutions for pose reconstruction.

Experimental Results

The experimental evaluation demonstrates the superior performance of the proposed framework compared to existing methods. The unified feature detection network significantly outperforms traditional approaches, achieving better accuracy and efficiency in keypoint and edge detection tasks under various real-world conditions. Quantitatively, the framework exhibits state-of-the-art accuracy in structured, distracted, and occluded environments, with reduced inference times that are critical for online surgical robot control.

Pose Reconstruction Performance

Robustness in pose reconstruction is validated through evaluations of RCM convergence using both qualitative and quantitative measures. The framework achieves high precision and consistency, reflected by low standard deviation results in spatial convergence tests. The method offers substantial improvements over PnP solutions and differentiable rendering approaches, demonstrating faster and more accurate pose estimation, which is crucial for practical deployment in surgical robotics.

Conclusion

This research presents a highly efficient and unified feature detection framework that enhances pose estimation accuracy for surgical robotic instruments in real-world MIS conditions. The model bridges significant gaps in feature detection and pose inference, offering robust solutions to longstanding challenges faced by current methods. Future directions for this work may include expanding the framework to accommodate dual-arm robotic systems and further addressing occlusion issues through advanced filtering techniques and probabilistic modeling approaches. Together, these contributions pave the way for improved robotic-assisted surgical interventions, with implications for both academic research and clinical applications in surgical robotics.