Automated registration algorithm between two point clouds

What is it about?

In this paper, we propose a novel local feature descriptor called local angle statistics histogram (LASH) for efficient 3D point cloud registration. LASH forms a description of local shape geometries by encoding their properties on angles between the normal vector of the point and the vector formed by the point and other points in its local neighbourhood. In addition, we propose a three-dimensional point cloud registration algorithm based on LASH. The registration algorithm firstly detects triangle matching points with consistent similarity ratios, and then aggregates each pair of triangular matching points into a set of matching points. We can use these matching sets to calculate multiple transformations between two point clouds. Finally, we use the error function to identify the best transformation and t o achieve coarse alignment of the two point clouds.

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Why is it important?

Our research shows that using triangle similarity ratio consistency can improve the accuracy of point cloud matching.