Exploiting Robust Estimators in Phase Correlation of 3D Point Clouds for 6 DoF Pose Estimation

Point cloud registration is a fundamental task in both remote sensing, photogrammetry, and computer vision, which is to align multiple point clouds to the same coordinate frame. Especially in LiDAR odometry, by conducting the transformation between two adjacent scans, the pose of the platform can be estimated. To be specific, the goal is to recover the relative six-degree-of-freedom (6 DoF) pose between the source point cloud and the target point cloud. In this paper, we explore the use of robust estimators in the phase correlation when registering two point clouds, enabling a 6 DoF pose estimation between point clouds in a sub-voxel accuracy. The estimator is a rule for calculating an estimate of a given quantity based on observed data. A robust estimator is an estimation rule that is insensitive to nonnormality and can estimate parameters of a given objective function from noisy observations. The proposed registration method is theoretically insensitive to noise and outliers than correspondence-based methods. Three core steps are involved in the method: transforming point clouds from the spatial domain to the frequency domain, decoupling of rotations and translations, and using robust estimators to estimate phase shifts. Since the estimation of transformation parameters lies in the calculation of phase shifts, robust estimators play a vital role in shift estimation accuracy. In this paper, we have tested the performance of six different robust estimators and provide comparisons and discussions on the contributions of robust estimators in the 3D phase correlation. Different point clouds from two urban scenarios and one indoor scene are tested. Results validate the proposed method can reach performance that predominant rotation and translation errors reaching less than 0.5° and 0.5 m, respectively. Moreover, the performance of various tested robust estimators is compared and discussed.