用外接摄像头解决关节镜导航中的定位漂移与尺度模糊问题
DualVision ArthroNav: Investigating Opportunities to Enhance Localization and Reconstruction in Image-based Arthroscopy Navigation via External Cameras
- 双相机融合:外置相机提供稳定定位,关节镜相机实现精细重建
- 定位误差仅1.09毫米,场景重建误差2.16毫米,视觉质量高
- 适合追求无侵入、高精度关节镜导航的临床与研发团队
关节镜手术可受益于提升空间感知、深度判断和视野范围的导航系统。然而,现有光学追踪方案对操作空间要求严格且干扰手术流程。基于视觉的替代方案虽更微创,但通常仅依赖单目关节镜摄像头,易受漂移、尺度模糊及快速运动或遮挡影响。我们提出DualVision ArthroNav,一种集成在关节镜上的刚性外置摄像头多相机导航系统。外置摄像头提供稳定的视觉里程计与绝对定位,单目关节镜视频实现密集场景重建。通过融合互补视角,系统解决了单目SLAM固有的尺度模糊与长期漂移问题,并确保鲁棒重定位。实验表明,系统有效补偿校准误差,平均绝对轨迹误差为1.09毫米;重建场景平均靶标注册误差为2.16毫米,视觉保真度高(SSIM=0.69,PSNR=22.19)。结果表明,该系统为关节镜导航提供了实用且低成本的解决方案,弥合了光学追踪与纯视觉系统之间的差距,推动可临床部署的全视觉导航发展。
原文摘要 · Abstract (English)
Arthroscopic procedures can greatly benefit from navigation systems that enhance spatial awareness, depth perception, and field of view. However, existing optical tracking solutions impose strict workspace constraints and disrupt surgical workflow. Vision-based alternatives, though less invasive, often rely solely on the monocular arthroscope camera, making them prone to drift, scale ambiguity, and sensitivity to rapid motion or occlusion. We propose DualVision ArthroNav, a multi-camera arthroscopy navigation system that integrates an external camera rigidly mounted on the arthroscope. The external camera provides stable visual odometry and absolute localization, while the monocular arthroscope video enables dense scene reconstruction. By combining these complementary views, our system resolves the scale ambiguity and long-term drift inherent in monocular SLAM and ensures robust relocalization. Experiments demonstrate that our system effectively compensates for calibration errors, achieving an average absolute trajectory error of 1.09 mm. The reconstructed scenes reach an average target registration error of 2.16 mm, with high visual fidelity (SSIM = 0.69, PSNR = 22.19). These results indicate that our system provides a practical and cost-efficient solution for arthroscopic navigation, bridging the gap between optical tracking and purely vision-based systems, and paving the way toward clinically deployable, fully vision-based arthroscopic guidance.
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