3D Spectral Domain Registration-Based Visual Servoing
Maxime Adjigble, Brahim Tamadazte, Cristiana de Farias, Rustam, Stolkin, Naresh Marturi

TL;DR
This paper introduces a spectral domain registration-based visual servoing method for 3D point clouds, enabling robust and efficient robot positioning even with partial data and noise, by leveraging spectral analysis for transformation estimation.
Contribution
The paper proposes a novel 3D spectral domain registration technique for visual servoing that works with partial point clouds and is robust to noise, improving over existing dense-data methods.
Findings
Effective registration with partial point clouds
Robust to sensor noise and occlusions
Successful robot arm positioning experiments
Abstract
This paper presents a spectral domain registration-based visual servoing scheme that works on 3D point clouds. Specifically, we propose a 3D model/point cloud alignment method, which works by finding a global transformation between reference and target point clouds using spectral analysis. A 3D Fast Fourier Transform (FFT) in R3 is used for the translation estimation, and the real spherical harmonics in SO(3) are used for the rotations estimation. Such an approach allows us to derive a decoupled 6 degrees of freedom (DoF) controller, where we use gradient ascent optimisation to minimise translation and rotational costs. We then show how this methodology can be used to regulate a robot arm to perform a positioning task. In contrast to the existing state-of-the-art depth-based visual servoing methods that either require dense depth maps or dense point clouds, our method works well with…
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Taxonomy
TopicsAdvanced Vision and Imaging · Optical measurement and interference techniques · Robotics and Sensor-Based Localization
