Comparison of methods for estimating the position of actuated instruments in flexible endoscopic surgery

The spatial configuration of actuated flexible instruments is fundamental for control applications in robotic noscar surgery. In these operations, the instruments are inserted in the channels of a flexible guide equipped with an endoscopic camera. In this paper we propose to estimate the position of the instruments of the Anubis platform (Karl Storz) using the endoscopic images provided by the embedded camera. In this system flexible instruments have 3-DOF (translation and rotation in the channel and deflection). Application of standard approaches for 3D location with such system do not provide good accuracy because of uncertainties on several model parameters. To cope with these uncertainties, supervised learning methods has been explored. With the help of colored visual markers attached to the instruments, the proposed approach consists of an image segmentation stage followed by a position estimation stage. Firstly, the markers are segmented in the images using an AdaBoost classifier manually trained on in-vivo images. Subsequently, the resulting blobs are used as input data of an approximation function trained using ground truth information provided by a magnetic sensor. A comparison with two other model-based methods showed the potentialities of such an approach on real devices.