After installing these packages, let's start the detection. What are the procedures involved? Here are the main steps:
- Building a CAD model of the object or capturing its 3D model
- Training the model
- Detecting the object using the trained model
The first step in the recognition process is building the 3D model of the desired object. We can do it using a CAD tool, or we can capture the real object using depth-sensing cameras. If the object is rigid, then the best procedure is CAD modelling, because it will have all the 3D information regarding the object. When we try to capture and build a 3D model, it may have errors and the mesh may not be look like the actual object because of the accumulation of errors in each stage. After building the object model, it will be uploaded to the object database. The next phase is the training of the uploaded object on the database. After training, we can start the detection process. The detection process will start capturing from the depth sensors and will match with the trained model in the database using different methods, such as Random Sample Consensus (RANSAC). If there is a match, it will marked the area and print the result. We can see the final detection output in Rviz.
Let's see how to add a mesh of an object to the object database. There are ORK tutorial packages that provide meshes of some objects, such as soda bottles. We can use one of these object and add it to the object database.