StereoCNC: A Stereovision-guided Robotic Laser System

Abstract

This paper proposes a stereovision-guided robotic laser system that can conduct laser ablation on targets selected by human operators in the color image, referred as StereoCNC. Two digital cameras are integrated into a previously developed robotic laser system to add a color sensing modality and formulate the stereovision. A calibration method is implemented to register the coordinate frames between stereo cameras and the laser system, modelled as a 3D-to-3D Least-squares problem. This problem is solved by a RANSAC-based 3D rigid transformation method and the calibration reprojection errors are used to characterize a 3D error field by Gaussian process regression. This regression error model is used to predict an error value for each data point of a stereo-reconstructed point cloud and an optimization problem is formulated to adjust the surgical site to a new position with minimum reprojection errors. Based on the calibrated system and the error model, a stereovision-guided laser-tissue removal pipeline is proposed to precisely locate, target, and ablate a surface region. The pipeline is validated by the experiments on phantoms with color texture and various geometric shapes. The overall targeting accuracy of the system achieves an average RMSE of 0.13±0.02 mm and maximum error of 0.34±0.06 mm, as measured by pre- and post-laser ablation images. The results show potential applications of using the developed stereovision-guided robotic system for superficial laser surgery, including dermatologic applications or removal of exposed tumorous tissue in neurosurgery.