Step 4, Part 2: Registration of Point Clouds at Different Angles

Now that the point clouds have been rotated to a relative coordinate system, we can perform the Iterative Closest Point Algorithm efficiently and accurately. Like I stated in the Introduction of Registration post, the Iterative Closest Point Algorithm is a built-in class in the Point Cloud Library that method used to minimize the distance between two clouds of points.

Once the ICP is performed, the algorithm produces a transformation matrix, which contains a translation vector, rotation matrix, an axis of rotation vector, and an angle of rotation. For the ICP algorithm to work efficiently, some parameters have to be adjusted. Here are some of the parameters:
- Input Cloud - this is the point cloud that is being transformed
- Input Target Cloud - this is the point cloud that the input cloud is being set to look like
- Max Correspondence Distance - any corresponding points in the point clouds further than this value will be ignored by the algorithm (0.1 is the value for my project)
-  Max Iterations - once the algorithm runs for x amount of times, the algorithm will terminate (1000 is the value in my project)
- Transformation Epsilon and Euclidean Fitness Epsilon - tolerance parameters for estimating the minimum distance between points in two different point clouds. Algorithm will terminate if the computed epsilons are lower than these values (1E-10 ans 1E-2 are values for my project, respectively)

Here are some pictures of the process from two point clouds at different angles to applying a rotation matrix, performing the Iterative Closest Point Algorithm, and concatenating the point clouds to produce one point cloud taken at different angles. Note that the test were applied to point clouds taken at 0,10,20,30 degrees:

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Before applying rotation

After applying rotation (924,903 points)

After applying ICP and voxel grid (44,209 points)

Note: the shadows of different point clouds start to mix during ICP and concatenation. This is not an error, but would prefer a evenly lighted room for a more accurate demonstration.

This process produces about 900,000 point clouds, making it inefficient for further processing. So for this case, it would be important to apply a filter to the final point cloud to down-sample the number of points in the cloud from around 900,000 points to about 45,000 points use another PCL built-in class called the Voxel Grid filter. The next step is to now obtain point clouds from the entire range of the Microsoft Kinect from -30 to 30 degrees in 10-degree increments, apply this process to the points clouds to  produce one combined point cloud and apply a Voxel Grid to the point cloud to decrease the number of points in the cloud.