PCL/OpenNI tutorial 3: Cloud processing (advanced)
Go to root: PhD-3D-Object-Tracking
Most of the techniques seen in the previous tutorial focused on preprocessing, that is, performing certain operations on the cloud to get it ready for further analysis or work. Downsampling, removing outliers, smoothing the surfaces, estimating the normals...
This new tutorial will teach you many interesting things that can be done with point clouds after the preparation step, such as registration, segmentation and model matching.
Registration
Registration is the technique of aligning two point clouds, like pieces of a puzzle. To be precise, the algorithm finds a set of correspondences between them, which would mean that there is an area of the scene that has been captured in both clouds. A linear transformation is then computed, which outputs a matrix that contains a rotation and a translation. These are the operations that you would apply to one of the clouds so it would get in place with respect to the other, with the intersecting areas overlapping.
The best results are achieved with clouds that are very similar, so you should try to minimize the transformations between them. Meaning, do not run like crazy with a Kinect in your hands, grabbing frames, and expect PCL to match the clouds. It is better to move the sensor gingerly and at steady intervals. If you use a robotic arm or a rotating tabletop with precise angles, even better.
Registration is a very useful technique because it lets you retrieve full, complete and continous models of a scene or object. It is also expensive for the CPU. Optimizations have been developed that allow to do cloud matching in real time with the GPU, like KinFu does.
The previous diagram illustrates how the procedure is done. There are mainly two different methods to choose from:
- ICP registration: ICP stands for Iterative Closest Point. It is an algorithm that will find the best transformation that minimizes the distance from the source point cloud to the target one. The problem is that it will do it by associating every point of the source cloud to its "twin" in the target cloud in a linear way, so it can be considered a brute force method. It the clouds are too big, the algorithm will take its time to finish, so try downsampling them first.
- Feature-based registration: the algorithm finds a set of keypoints in each cloud, computes a descriptor for each (we mentioned what descriptors were in the previous tutorial; they are like individual signatures of points), and then performs a search to see if the clouds have keypoints in common. If true, a transformation is computed. For better results, enough correspondences must be found.
Go to root: PhD-3D-Object-Tracking
Links to articles:
PCL/OpenNI tutorial 0: The very basics
PCL/OpenNI tutorial 1: Installing and testing
PCL/OpenNI tutorial 2: Cloud processing (basic)
PCL/OpenNI tutorial 3: Cloud processing (advanced)
