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RCM-SLAM: Visual localisation and mapping under remote centre of motion constraints

Francisco Vasconcelos, Evangelos B. Mazomenos, John D. Kelly, Danail Stoyanov

Year
2019
Citations
9

Abstract

In robotic surgery the motion of instruments and the laparoscopic camera is constrained by their insertion ports, i. e. a remote centre of motion (RCM). We propose a Simultaneous Localisation and Mapping (SLAM) approach that estimates laparoscopic camera motion under RCM constraints. To achieve this we derive a minimal solver for the absolute camera pose given two 2D-3D point correspondences (RCM-PnP) and also a bundle adjustment optimiser that refines camera poses within an RCM-constrained parameterisation. These two methods are used together with previous work on relative pose estimation under RCM [1] to assemble a SLAM pipeline suitable for robotic surgery. Our simulations show that RCM-PnP outperforms conventional PnP for a wide noise range in the RCM position. Results with video footage from a robotic prostatectomy show that RCM constraints significantly improve camera pose estimation.

Keywords

Computer visionArtificial intelligenceComputer scienceSimultaneous localization and mappingBundle adjustmentSolverPoseMotion estimationStructure from motionNoise (video)

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