论文标题
部分可观测时空混沌系统的无模型预测
Translation Invariant Global Estimation of Heading Angle Using Sinogram of LiDAR Point Cloud
论文作者
论文摘要
全球点云注册是定位的重要模块，在没有初始值的情况下，全球估算旋转的主要困难存在。借助重力比对，可以将点云登记的自由度减小到4DOF，其中仅旋转估计只需要标题角。在本文中，我们提出了一种快速准确的全局标题角度估计方法，用于重力对准点云。我们的关键思想是，我们基于ra换变换生成了一个翻译不变表示形式，使我们能够通过圆形互相关解决全球脱钩的头部角度。此外，对于具有不同分布的点云之间的标题角度估计，我们将此标题估计器作为一个可区分的模块来训练特征提取网络最终到端。实验结果验证了所提出的方法在标题角度估计中的有效性，并且与其他方法相比显示出更好的性能。
Global point cloud registration is an essential module for localization, of which the main difficulty exists in estimating the rotation globally without initial value. With the aid of gravity alignment, the degree of freedom in point cloud registration could be reduced to 4DoF, in which only the heading angle is required for rotation estimation. In this paper, we propose a fast and accurate global heading angle estimation method for gravity-aligned point clouds. Our key idea is that we generate a translation invariant representation based on Radon Transform, allowing us to solve the decoupled heading angle globally with circular cross-correlation. Besides, for heading angle estimation between point clouds with different distributions, we implement this heading angle estimator as a differentiable module to train a feature extraction network end- to-end. The experimental results validate the effectiveness of the proposed method in heading angle estimation and show better performance compared with other methods.
