For me Computer Vision is one of the most exciting and important topics in robotics. Humans get 80% of all information about the world using their eyes. So the ability to collect visual information is a big advantage for any robot. The basic visual information our brain supplies to our mind is what objects are around and where they are. Let’s consider how robots do that.
Computer vision object detection consists of the following major steps:
2. Extracting points that are potentially related to the looking object. This robot applies a gradient filter to detect potential borders – the green dots on the picture.
3. Segmenting the extracted points. Removing small segments. Roughness of the surfaces also have its mini-borders on the image. The “real” object have a long solid border that helps to distinguish it.
4. Constructing objects from the extracted segments using specific patterns. The robot is interested only in objects he can pick up by his gripper, the objects should not be too small or too large. Also black gripper parts that are on the bottom right and left of the image should not be detected as objects.
6. Knowing the configuration of the robot’s neck the robot calculates position and direction of the camera, using that, he calculates the location of the detected objects on the floor. The accuracy of the detection by this robot is some millimeters!
Calculating the location of the objects the robot can easily locate his head to grab them.
Follow my posts, you will read the details of every step of this process.