Three new Iterative Closest Point variant-methods that improve scan matching for surface mining terrain

The Iterative Closest Point (ICP) algorithm seeks to minimize the misalignment between two point cloud data sets. A limitation of many ICP algorithms is that they work well for some contexts, yet perform poorly in others. Previous work has suggested that the ability of ICP variants to find correspondence was hindered by the presence of geometric disorder in the scene. This paper introduces three new methods based on characterizing the geometric properties of a point using information of its nearest neighbours. Two methods are entropy based and quantify the geometric disorder (eigentropy) in order to improve the filtering of data and thereby remove points that are likely to provide spurious associations. The third method is a point matching method using normals to preferentially work with planar areas of a point cloud. A set of 73,728 ICP variants obtained by combination/permutation of 26 methods are evaluated. These variants were evaluated using a scan matching exercise requiring construction of terrain maps based on data from a mobile sensing platform in an open-cut mining environment. The proposed methods improve ICP performance, as measured by accuracy, precision, and computational efficiency. Notably, five ICP variants, each featuring the new methods of this paper, simultaneously met the solution requirements for three different terrain scenes. It is asserted that being able to characterize the geometric disorder in the point clouds improves the capability of ICP to establish associations between points.