3D Spectral Domain Registration-Based Visual Servoing

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 partial point clouds and can effectively handle larger transformations between the reference and the target positions. Furthermore, the use of spectral data (instead of spatial data) for transformation estimation makes our method robust to sensor-induced noise and partial occlusions. We validate our approach by performing experiments using point clouds acquired by a robot-mounted depth camera. Obtained results demonstrate the effectiveness of our visual servoing approach.