Ophthalmic microsurgery is known to be a challenging operation, which requires very precise and dexterous manipulation. Image guided robot-assisted surgery is a promising solution that brings significant improvements in outcomes and reduces the physical limitations of human surgeons. However, this technology must be further developed before it can be routinely used in clinics. One of the problems is the lack of proper calibration between the robotic manipulator and appropriate imaging device. In this letter, we developed a flexible framework for hand-eye calibration of an ophthalmic robot with a microscope-integrated optical coherence tomography (OCT) without any markers. The proposed method consists of three main steps are following: First, we estimate the OCT calibration parameters; second, with micro-scale displacements controlled by the robot, we detect and segment the needle tip in three-dimensional OCT volume and; finally, we find the transformation between the coordinate system of the OCT camera and the coordinate system of the robot. We verified the capability of our framework in ex-vivo pig eye experiments and compared the results with a reference method (marker-based). In all experiments, our method showed a small difference from the marker based method, with a mean calibration error of 9.2 μm and 7.0 μm, respectively. Additionally, the noise test shows the robustness of the proposed method.
Hand-eye calibration is an active research topic in robotics and has been investigated by many researchers. In particular, the seminal work of since 1980s @cite_5 . There exist several off-the-shelf calibration solutions that implement Tsai's method (e.g. ViSP library @cite_25 , which is designed for visual servoing applications). These existing off-the-shelf calibration solutions can be easily integrated into standard camera vision systems and allow fast prototyping. However, they require separate measurements and extra equipment (calibration grids, markers,...). In some medical scenarios, it is not practical to place the calibration objects or markers inside the tissue or bodily organ. In addition, medical imaging modalities (e.g OCT and ultrasound) have different imaging principles, which leads to a different camera calibration process.
@cite_5 @cite_25
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In a robot mounted camera arrangement, hand-eye calibration estimates the rigid relationship between the robot and camera coordinate frames. Most hand-eye calibration techniques use a calibration object to estimate the relative transformation of the camera in several views of the calibration object and link these to the forward kinematics of the robot to compute the hand-eye transformation. Such approaches achieve good accuracy for general use but for applications such as robotic assisted minimally invasive surgery, acquiring a calibration sequence multiple times during a procedure is not practical. In this paper, we present a new approach to tackle the problem by using the robotic surgical instruments as the calibration object with well known geometry from CAD models used for manufacturing. Our approach removes the requirement of a custom sterile calibration object to be used in the operating room and it simplifies the process of acquiring calibration data when the laparoscope is constrained to move around a remote centre of motion. This is the first demonstration of the feasibility to perform hand-eye calibration using components of the robotic system itself and we show promising validation results on synthetic data as well as data acquired with the da Vinci Research Kit. We present a novel on-line approach for extrinsic robot-camera calibration, a process often referred to as hand-eye calibration, that uses object pose estimates from a real-time model-based tracking approach. While off-line calibration has seen much progress recently due to the incorporation of bundle adjustment techniques, on-line calibration still remains a largely open problem. Since we update the calibration in each frame, the improvements can be incorporated immediately in the pose estimation itself to facilitate object tracking. Our method does not require the camera to observe the robot or to have markers at certain fixed locations on the robot. To comply with a limited computational budget, it maintains a fixed size configuration set of samples. This set is updated each frame in order to maximize an observability criterion. We show that a set of size 20 is sufficient in real-world scenarios with static and actuated cameras. With this set size, only 100 microseconds are required to update the calibration in each frame, and we typically achieve accurate robot-camera calibration in 10 to 20 seconds. Together, these characteristics enable the incorporation of calibration in normal task execution. A new technique for three-dimensional (3D) camera calibration for machine vision metrology using off-the-shelf TV cameras and lenses is described. The two-stage technique is aimed at efficient computation of camera external position and orientation relative to object reference coordinate system as well as the effective focal length, radial lens distortion, and image scanning parameters. The two-stage technique has advantage in terms of accuracy, speed, and versatility over existing state of the art. A critical review of the state of the art is given in the beginning. A theoretical framework is established, supported by comprehensive proof in five appendixes, and may pave the way for future research on 3D robotics vision. Test results using real data are described. Both accuracy and speed are reported. The experimental results are analyzed and compared with theoretical prediction. Recent effort indicates that with slight modification, the two-stage calibration can be done in real time. 3D UltraSound (US) probes are used in clinical applications for their ease of use and ability to obtain intra-operative volumes. In surgical navigation applications a calibration step is needed to localize the probe in a general coordinate system. This paper presents a new hand-eye calibration method using directly the kinematic model of a robot and US volume registration data that does not require any 3D localizers. First results show a targeting error of 2.34 mm on an experimental setup using manual segmentation of five beads in ten US volumes.