Three‐dimensional Scan Registration using Curvelet Features in Planetary Environments

Topographic mapping in planetary environments relies on accurate three‐dimensional (3D) scan registration methods. However, most global registration algorithms relying on features such as fast point feature histograms and Harris‐3D show poor alignment accuracy in these settings due to the poor structure of the Mars‐like terrain, and the variable‐resolution, occluded, sparse range data that are difficult to register without some a priori knowledge of the environment. In this paper, we propose an alternative approach to 3D scan registration using the curvelet transform that performs multiresolution geometric analysis to obtain a set of coefficients indexed by scale (coarsest to finest), angle, and spatial position. Features are detected in the curvelet 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 reprojected correspondences in the metric space. Experimental results on a publicly available dataset of a planetary analogue indoor facility, as well as simulated and real‐world scans from Neptec Design Group's IVIGMS 3D laser rangefinder at the outdoor CSA Mars yard, demonstrate improved performance over existing methods in the challenging sparse Mars‐like terrain.