Abstract
Estimating the 3-D pose of instruments is an important part of robotic minimally invasive surgery for automation of basic procedures as well as providing safety features, such as virtual fixtures. Image-based methods of 3-D pose estimation provide a non-invasive low cost solution compared with methods that incorporate external tracking systems. In this paper, we extend our recent work in estimating rigid 3-D pose with silhouette and optical flow-based features to incorporate the articulated degrees-of-freedom (DOFs) of robotic instruments within a gradient-based optimization framework. Validation of the technique is provided with a calibrated ex-vivo study from the da Vinci Research Kit (DVRK) robotic system, where we perform quantitative analysis on the errors each DOF of our tracker. Additionally, we perform several detailed comparisons with recently published techniques that combine visual methods with kinematic data acquired from the joint encoders. Our experiments demonstrate that our method is competitively accurate while relying solely on image data.
Highlights
M INIMALLY invasive surgery (MIS) has provided surgeons with a less invasive method of accessing the surgical site with a cost of having less control and information about the operation compared with open surgery
We present a novel system of tracking the articulated DOFs of surgical robotic instruments in 3D using a fully vision-based region and point based solution
Our system trivially extends to different instrument models and color schemes which greatly increases the range of robotic systems it can be tested on
Summary
M INIMALLY invasive surgery (MIS) has provided surgeons with a less invasive method of accessing the surgical site with a cost of having less control and information about the operation compared with open surgery. Laparoscopic instruments reduce the surgeon’s dexterity and ability to sense force feedback from applied tissue pressure and the limited field of view of the surgical camera makes self-localization challenging and increases the cognitive workload on the surgeon. The learning curve for MIS is steep with surgeons taking significant periods of time to obtain mastery of the techniques [1]. Computer assisted surgery (CAS) and robotics have played a large role in reducing these complications through advanced instruments, control and visualization.
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