title: A Filtering Approach for Image-Guided Surgery With a Highly Articulated Surgical Snake Robot.
abstract: OBJECTIVE
  The objective of this paper is to introduce a probabilistic filtering approach to estimate the pose and internal shape of a highly flexible surgical snake robot during minimally invasive surgery.
  
  
  METHODS
  Our approach renders a depiction of the robot that is registered to preoperatively reconstructed organ models to produce a 3-D visualization that can be used for surgical feedback. Our filtering method estimates the robot shape using an extended Kalman filter that fuses magnetic tracker data with kinematic models that define the motion of the robot. Using Lie derivative analysis, we show that this estimation problem is observable, and thus, the shape and configuration of the robot can be successfully recovered with a sufficient number of magnetic tracker measurements.
  
  
  RESULTS
  We validate this study with benchtop and in-vivo image-guidance experiments in which the surgical robot was driven along the epicardial surface of a porcine heart.
  
  
  CONCLUSIONS
  This paper introduces a filtering approach for shape estimation that can be used for image guidance during minimally invasive surgery.
  
  
  CONCLUSIONS
  The methods being introduced in this paper enable informative image guidance for highly articulated surgical robots, which benefits the advancement of robotic surgery.
authors.forename: Stephen
authors.initials: S
authors.lastname: Tully
authors.forename: Howie
authors.initials: H
authors.lastname: Choset
mesh_terms: D000465:Algorithms; D000818:Animals; D004867:Equipment Design; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008962:Models, Theoretical; D010496:Pericardium / Q000601:surgery; D065287:Robotic Surgical Procedures / Q000295:instrumentation* / Q000379:methods; D025321:Surgery, Computer-Assisted / Q000295:instrumentation* / Q000379:methods; D013552:Swine