# Vision-Based Shape Reconstruction of Soft Continuum Arms Using a Geometric Strain Parametrization

arXiv: https://arxiv.org/abs/2011.09106 · HTML: https://mlsanity.com/paper/2011.09106/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Nov 2020.

## Built on by (most cited first, 2 of 2)

- [Image-based Pose Estimation and Shape Reconstruction for Robot Manipulators and Soft, Continuum Robots via Differentiable Rendering](https://mlsanity.com/paper/2302.14039/) (2023)
- [Realtime Robust Shape Estimation of Deformable Linear Object](https://mlsanity.com/paper/2403.16146/) (2024)

## Abstract (arXiv)

Interest in soft continuum arms has increased as their inherent material elasticity enables safe and adaptive interactions with the environment. However to achieve full autonomy in these arms, accurate three-dimensional shape sensing is needed. Vision-based solutions have been found to be effective in estimating the shape of soft continuum arms. In this paper, a vision-based shape estimator that utilizes a geometric strain based representation for the soft continuum arm's shape, is proposed. This representation reduces the dimension of the curved shape to a finite set of strain basis functions, thereby allowing for efficient optimization for the shape that best fits the observed image. Experimental results demonstrate the effectiveness of the proposed approach in estimating the end effector with accuracy less than the soft arm's radius. Multiple basis functions are also analyzed and compared for the specific soft continuum arm in use.