Translation Invariant Global Estimation of Heading Angle Using Sinogram of LiDAR Point Cloud
This addresses the challenge of rotation estimation in point cloud registration for localization, particularly in robotics or autonomous systems, but appears incremental as it builds on existing gravity-alignment techniques.
The paper tackles the problem of global heading angle estimation for gravity-aligned LiDAR point clouds by proposing a fast and accurate method using a translation-invariant representation based on Radon Transform, which solves the angle globally with circular cross-correlation and shows better performance in experiments compared to other methods.
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.