text: This paper presents a mutual information (MI) based algorithm for the estimation of full 6-degree-of-freedom (DOF) rigid body transformation between two overlapping point clouds. We first divide the scene into a 3D voxel grid and define simple to compute features for each voxel in the scan. The two scans that need to be aligned are considered as a collection of these features and the MI between these voxelized features is maximized to obtain the correct alignment of scans. We have implemented our method with various simple point cloud features (such as number of points in voxel, variance of z-height in voxel) and compared the performance of the proposed method with existing point-to-point and point-to-distribution registration methods. We show that our approach has an efficient and fast parallel implementation on GPU, and evaluate the robustness and speed of the proposed algorithm on two real-world datasets which have variety of dynamic scenes from different environments.
  @cite_30: 
  @cite_10: Matching 2D range scans is a basic component of many localization and mapping algorithms. Most scan match algorithms require finding correspondences between the used features, i.e. points or lines. We propose an alternative representation for a range scan, the normal distributions transform. Similar to an occupancy grid, we subdivide the 2D plane into cells. To each cell, we assign a normal distribution, which locally models the probability of measuring a point. The result of the transform is a piecewise continuous and differentiable probability density, that can be used to match another scan using Newton's algorithm. Thereby, no explicit correspondences have to be established. We present the algorithm in detail and show the application to relative position tracking and simultaneous localization and map building (SLAM). First results on real data demonstrate, that the algorithm is capable to map unmodified indoor environments reliable and in real time, even without using odometry data.
  @cite_33: This paper presents the basis of a new system for making detailed 3D models of underground tunnels. The system is to be used for automated control of mining vehicles. We describe some alternative methods for matching several partial scans, and their applicability for making a complete model of a mine environment
summary: Another widely used algorithm is Normal Distribution Transform (NDT) which was initially proposed for 2D data @cite_10 and was later extended to three-dimensions. 3D-NDT @cite_33 is a point-to-distribution method in which the maximum likelihood estimate (MLE) of points in @math is maximized over the distribution of points in a gaussian mixture model derived from @math . Supervoxel based NDT (SV-NDT) @cite_30 is an extension to 3D-NDT in which segmentation on the basis of local-spatial structure is done over @math before creating a GMM model.