Step 4, Part 3: Reconstructing an Entire Scene using Point Cloud Registration

Now is the time to put the process decided in the previous two posts to the test. I used the Microsoft Kinect to capture point clouds from -30 degrees all the way up to 30 degrees in 10-degree increments resulting in seven point clouds at different angles. The process is a little different now because moving all of the point clouds up from -30 degrees to 30 degrees and apply the ICP to each of them will make the process inefficient as the process goes on as the number of point start to increase with each new point cloud being added. In this case, the point clouds from -30 to 0 degrees will be combined together and point clouds from 10 to 30 degrees will be combined together, resulting in these two point clouds being combined at the end to produce one single point cloud. Here are some pictures of the results of the process:

Before applying rotation

After applying rotation (1,664,196 points)

After applying ICP to top and bottom halves of scene (Top -  678,301 points / Bottom - 985,895 points / 1,664,196 points Total)

After applying final ICP and combining all of the point clouds (1,664,196 points)

The final resulting point cloud has around 1.6 million points so applying the voxel grid is very useful for this point cloud. Now that we know that Point Cloud Registration is efficient and most importantly works, we can move on to using multiple Kinect and using the same process as described in the previous posts.