Learning to Calibrate - Estimating the Hand-eye Transformation without Calibration Objects

Hand-eye calibration is a method to determine the transformation linking between the robot and camera coordinate systems. Conventional calibration algorithms use a calibration grid to determine camera poses, corresponding to the robot poses, both of which are used in the main calibration procedure. Although such methods yield good calibration accuracy and are suitable for offline applications, they are not applicable in a dynamic environment such as robotic-assisted minimally invasive surgery (RMIS) because changes in the setup can be disruptive and time-consuming to the workflow as it requires yet another calibration procedure. In this paper, we propose a neural network-based hand-eye calibration method that does not require camera poses from a calibration grid but only uses the motion from surgical instruments in a camera frame and their corresponding robot poses as input to recover the hand-eye matrix. The advantages of using neural network are that the method is not limited by a single rigid transformation alignment and can learn dynamic changes correlated with kinematics and tool motion/interactions. Its loss function is derived from the original hand-eye transformation, the re-projection error and also the pose error in comparison to the remote centre of motion. The proposed method is validated with data from da Vinci Si and the results indicate that the designed network architecture can extract the relevant information and estimate the hand-eye matrix. Unlike the conventional hand-eye approaches, it does not require camera pose estimations which significantly simplifies the hand-eye problem in RMIS context as updating the hand-eye relationship can be done with a trained network and sequence of images. This introduces a potential of creating a hand-eye calibratio

Hand-eye calibration for robotic assisted minimally invasive surgery without a calibration object

In a robot mounted camera arrangement, handeye 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

Hand-eye calibration with a remote centre of motion

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 letter, 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 ill-conditioned 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

Adjoint Transformation Algorithm for Hand-Eye Calibration with Applications in Robotic Assisted Surgery

Hand-eye calibration aims at determining the unknown rigid transformation between the coordinate systems of a robot arm and a camera. Existing hand-eye algorithms using closed-form solutions followed by iterative non-linear refinement provide accurate calibration results within a broad range of robotic applications. However, in the context of surgical robotics hand-eye calibration is still a challenging problem due to the required accuracy within the millimetre range, coupled with a large displacement between endoscopic cameras and the robot end-effector. This paper presents a new method for hand-eye calibration based on the adjoint transformation of twist motions that solves the problem iteratively through alternating estimations of rotation and translation. We show that this approach converges to a solution with a higher accuracy than closed form initializations within a broad range of synthetic and real experiments. We also propose a stereo hand-eye formulation that can be used in the context of both our proposed method and previous state-of-the-art closed form solutions. Experiments with real data are conducted with a stereo laparoscope, the KUKA robot arm manipulator, and the da Vinci surgical robot, showing that both our new alternating solution and the explicit representation of stereo camera hand-eye relations contribute to a higher calibration accuracy

Examining in vivo tympanic membrane mobility using smart phone video-otoscopy and phase-based eulerian video magnification

The tympanic membrane (TM) is the bridging element between the pressure waves of sound in air and the ossicular chain. It allows for sound to be conducted into the inner ear, achieving the human sense of hearing. Otitis media with effusion (OME, commonly referred to as ‘glue ear’) is a typical condition in infants that prevents the vibration of the TM and causes conductive hearing loss, this can lead to stunting early stage development if undiagnosed. Furthermore, OME is hard to identify in this age group; as they cannot respond to typical audiometry tests. Tympanometry allows for the mobility of the TM to be examined without patient response, but requires expensive apparatus and specialist training. By combining a smartphone equipped with a 240 frames per second video recording capability with an otoscopic clip-on accessory, this paper presents a novel application of Eulerian Video Magnification (EVM) to video-otology, that could provide assistance in diagnosing OME. We present preliminary results showing a spatio-temporal slice taken from an exaggerated video visualization of the TM being excited in vivo on a healthy ear. Our preliminary results demonstrate the potential for using such an approach for diagnosing OME under visual inspection as alternative to tympanometry, which could be used remotely and hence help diagnosis in a wider population pool