Fast range-independent spherical subsampling of 3D laser scanner points and data reduction performance evaluation for scene registration

摘要

Three-dimensional laser range-finders are increasingly being incorporated into applications, such as mobile robotics, that require real-time registration of scene data. However, the computational costs of adaptive range-dependent data selection and point cloud matching grow significantly with the number of points. Therefore, fast range-independent subsampling by uniform or random data reduction is usually performed at a preprocessing step. The paper proposes a new range-independent subsampling algorithm that is more effective for the widely used spherical scanning mechanism. As this type of device measures the ranges by composition of two rotations, it samples certain directions with a higher density, which can distort the registration optimization process. The proposed solution uses sensor characteristics to equalize the measure-direction density of the reduced point cloud. The paper also addresses performance assessment of subsampling methods by contributing three benchmark criteria that do not rely on a particular registration technique: one considers the ground truth transformation between two scans, and the other two are based on the analysis of a single scan. The advantages of spherical subsampling are analyzed through a comparison of range-independent methods and a simple range-dependent one with real scans from three representative scenes (urban, natural, and indoors).