aiqbench analysis
This research proposes a marker-free shape registration algorithm for continuum robots, enabling automated reconstruction of their deformed backbones from image data with sub-millimeter accuracy. The method significantly accelerates model validation and reference-data acquisition for applications in aerospace, inspection, and minimally invasive medicine by replacing manual photogrammetry with an optimized pipeline running in under half a second.
Reconstruction of continuum robots by marker-free shape registration of image data using a kinematic model
arXiv:2405.15336v2 Announce Type: replace Abstract: Continuum robots are slender, flexible manipulators that navigate confined, curved workspaces and are gaining traction in aerospace, inspection, automation, and minimally invasive medical applications. Predicting their shape from physics-based models alone remains challenging, making accurate measurement of the deformed backbone essential for model validation and reference-data acquisition. We present an optimization-based shape-registration algorithm that fits a parametric three-dimensional curve directly to image observations within a photogrammetric pipeline, targeting marker-free measurement rather than sensing under occlusion. By matching reconstruction points to robot pixels, the method requires no prior knowledge of the robot's location in each image. Across most configurations, the estimated backbone deviates from ground truth by less than 1 mm (0.67% of the robot's length). On real concentric-tube continuum robots, the reconstruction agrees with ten discrete manual photogrammetric measurements over 18 configurations, while replacing the manual procedure with an automated pipeline that runs in roughly 0.5 s per configuration.
Abstract:Continuum robots are slender, flexible manipulators that navigate confined, curved workspaces and are gaining traction in aerospace, inspection, automation, and minimally invasive medical applications. Predicting their shape from physics-based models alone remains challenging, making accurate measurement of the deformed backbone essential for model validation and reference-data acquisition. We present an optimization-based shape-registration algorithm that fits a parametric three-dimensional curve directly to image observations within a photogrammetric pipeline, targeting marker-free measurement rather than sensing under occlusion. By matching reconstruction points to robot pixels, the method requires no prior knowledge of the robot's location in each image. Across most configurations, the estimated backbone deviates from ground truth by less than 1 mm (0.67% of the robot's length). On real concentric-tube continuum robots, the reconstruction agrees with ten discrete manual photogrammetric measurements over 18 configurations, while replacing the manual procedure with an automated pipeline that runs in roughly 0.5 s per configuration.
24 pages, 14 figures. v2: accepted version, substantially revised and extended. Title changed from "An iterative closest point algorithm for marker-free 3D shape registration of continuum robots". New: comparison with differentiable rendering, sweep over 10,645 configurations, errors relative to robot length, real-image validation on 18 configurations of two CTCRs. Method unchanged
Robotics (cs.RO); Image and Video Processing (eess.IV)
I.4.5; I.2.9; G.1.6
arXiv:2405.15336 [cs.RO]
(or arXiv:2405.15336v2 [cs.RO] for this version)
https://doi.org/10.1080/13873954.2026.2716623 DOI(s) linking to related resources
From: Matthias K. Hoffmann [view email] [v1] Fri, 24 May 2024 08:17:40 UTC (2,421 KB) [v2] Wed, 19 Aug 2026 13:05:27 UTC (8,712 KB)
Full text under the publisher's open licence · 248 words
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