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.
翻译:本文提出了一种基于频谱域变换的点云视觉伺服方案,能够对三维点云进行操作。具体地,我们提出了一种 3D 模型/点云对齐方法,通过使用谱分析法找到参考点云和目标点云之间的全局变换。使用 R3 中的 3D 快速傅里叶变换进行平移估计,而使用 SO(3) 中实球谐函数进行旋转估计,从而得出一个解耦合的 6 自由度控制器。我们使用梯度上升优化算法来最小化平移和旋转代价来实现控制。我们在文中还说明了如何利用该方法调节机械臂进行定位任务。与现有基于深度的视觉伺服方法不同,本方法能够有效处理部分点云,尤其能够更好地应对参考位置和目标位置之间的变换。使用谱数据进行变换估计使本方法对传感器引起的噪声和部分遮挡具备稳健性。我们通过机器人装配的深度相机拍摄的点云进行实验验证了我们的方法,并得到有效结果。