3D Scan Registration Using Curvelet Features

Abstract

Scan registration methods can often suffer from convergence and accuracy issues when the scan points are sparse or the environment violates the assumptions the methods are founded on. We propose an alternative approach to 3D scan registration using the curve let transform that performs multi-resolution geometric analysis to obtain a set of coefficients indexed by scale (coarsest to finest), angle and spatial position. Features are detected in the curve let domain to take advantage of the directional selectivity of the transform. A descriptor is computed for each feature by calculating the 3D spatial histogram of the image gradients, and nearest neighbor based matching is used to calculate the feature correspondences. Correspondence rejection using Random Sample Consensus identifies inliers, and a locally optimal Singular Value Decomposition-based estimation of the rigid-body transformation aligns the laser scans given the re-projected correspondences in the metric space. Experimental results on a publicly available dataset of planetary analogue facility demonstrates improved performance over existing methods.