Pose Quality Prediction for Vision Guided Robotic Shoulder Arthroplasty

Surgical assistive robots offer the potential for drastically improved patient outcomes through more accurate, more repeatable surgical procedures like shoulder arthroplasty operations. Existing robotic systems typically rely on optical marker tracking and require invasive marker attachment for localization, complicating the surgical workflow and patient recovery. But moving towards a markerless system is very challenging, both because of the absolute difficulty and the large variation in localization conditions across thousands of surgical procedures. In this paper we propose an alternative approach: rather than try to create a "perfect" and fully generalizable markerless localization system, instead create a reliable and trustworthy localization system that is able to continually self-assess the likely quality of its localization estimates, and act accordingly. We propose a lightweight method for predicting vision-based pose estimation performance using internal pipeline artifacts (without needing external ground truth from a marker-based system). Using extensive real robot experiments with challenging actual imagery from surgery, we demonstrate our prediction system accurately self-characterizes the localization system's performance across a wide range of localization conditions, and demonstrate that this prediction system generalizes to a range of surgical conditions. We then show how online performance prediction can drive active robot navigation that minimizes localization error, reducing target pose estimation error by 96.1% for rotation and 96.7% for translation compared to rejected alternative trajectories.