Hand-Eye Calibration With a Remote Centre of Motion

Abstract

In the eye-in-hand robot configuration, hand-eye calibration plays a vital role in completing the link between the robot and camera coordinate systems. Calibration algorithms are mature and provide accurate transformation estimations for an effective camera-robot link but rely on a sufficiently wide range of calibration data to avoid errors and degenerate configurations. This can be difficult in the context of keyhole surgical robots because they are mechanically constrained to move around a remote centre of motion (RCM) which is located at the trocar port. The trocar limits the range of feasible calibration poses that can be obtained and results in ill-conditioned hand-eye constraints. In this paper, we propose a new approach to deal with this problem by incorporating the RCM constraints into the hand-eye formulation. We show that this not only avoids illconditioned constraints but is also more accurate than classic hand-eye calibration with a free 6DoF motion, due to solving simpler equations that take advantage of the reduced DoF. We validate our method using simulation to test numerical stability and a physical implementation on an RCM constrained KUKA LBR iiwa 14 R820 equipped with a NanEye stereo camera.

A miniature robotic steerable endoscope for maxillary sinus surgery called PliENT

“…2 b). The NanEye camera is a relatively cheap chip-on tip camera that was already used in previous studies on surgical instrumentation 39 , 40 .…”

Robotic Tool Tracking under Partially Visible Kinematic Chain: A Unified Approach

“…Adjoint transformations from twist motions have also been applied to converge to solutions with high accuracy [43], [44]. In the case of RCM robots, reduction of the computational complexity has been found [45], [46]. Similar to the previously described visual servoing techniques, the robot camera arm can also be controlled through online jacobian estimation [47], [48].…”

“…The Lumped Errors of the two surgical robotic tools were tracked using the same point and edge features as described in the surgical tool tracking experiments. Their positions were regulated using the same controller as described in (44) and (45) t is set using inverse kinematics and joint level regulators which use the noisy joint angle readings, qi t , as feedback. The controller effectively regulates the surgical robotic tools along the generated motion plan to complete the task of suture needle regrasping as shown in Fig.…”

The Challenge of Augmented Reality in Surgery

“…There are standard implementations in OpenCV and MatLab that tend to be used [27,28]. For video see-through, the methods for calibration are well-established and most studies suggest that registration is a larger source of error [29] but perhaps new constraints that incorporate the position of the trocar point can reduce error [30].…”

“…Live tracking and update of the image as the surgeon's view changes is required for accurate AR as the scene and the observer change their relationship. In the case of robot-assisted procedures the camera motion can potentially be provided by the robot kinematics although correction is likely needed because error propagates between the robot encoder coordinates and the camera frame [30]. For non-robotic MIS an external tracker is usually attached to the proximal end of the laparoscope and used to estimate the camera motion [18].…”