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A Learning Algorithm for Visual Pose Estimation of Continuum Robots Austin Reiter, Roger E. Goldman, Andrea Bajo, Konstantinos Iliopoulos, Nabil Simaan, and Peter K. Allen
 

Summary: A Learning Algorithm for Visual Pose Estimation of Continuum Robots
Austin Reiter, Roger E. Goldman, Andrea Bajo, Konstantinos Iliopoulos, Nabil Simaan, and Peter K. Allen
Abstract-- Continuum robots offer signicant advantages for
surgical intervention due to their down-scalability, dexterity,
and structural flexibility. While structural compliance offers
a passive way to guard against trauma, it necessitates robust
methods for online estimation of the robot configuration in
order to enable precise position and manipulation control.
In this paper, we address the pose estimation problem by
applying a novel mapping of the robot configuration to a feature
descriptor space using stereo vision. We generate a mapping of
known features through a supervised learning algorithm that
relates the feature descriptor to known ground truth. Features
are represented in a reduced sub-space, which we call eigen-
features. The descriptor provides some robustness to occlusions,
which are inherent to surgical environments, and the methodol-
ogy that we describe can be applied to multi-segment continuum
robots for closed-loop control. Experimental validation on a
single-segment continuum robot demonstrates the robustness
and efficacy of the algorithm for configuration estimation.

  

Source: Allen, Peter K. - Department of Computer Science, Columbia University

 

Collections: Engineering; Computer Technologies and Information Sciences