Identifying Objects with Related Angles Using Vision-Based System Integrated with Service Robots

Abstract

Manipulation of an object can be done with the collaboration of a human to a robot by properly introducing the object. To do this easily, we can model the object inside the robot’s head and let the robot identify it using some sensors and cameras. But when it comes to the real world, robots should have some mechanism to recognize objects in a perspective frame with angels. In this research authors will present a strategy to identify the unknown objects using a vision-based system and with the perspective angles of the detected object and the system is integrated with service robots. This will go in a way when the robot should be able to identify the objects around the robot in an asynchronous manner with rotational angles and the pitch and roll angles, perspective to the robot standing surface. The research will be based on Artificial intelligence, Machine learning, and Robotics.For the identification process, a few ways can be used. Vision-based identification using color and depth images from an RGB camera, and this research is mainly based on this RGB and depth feature integrated with YoloV5. And there are some other ways to identify objects like using LiDAR laser scanner. However, this learning process, should have a stable object to model and train the object. After the object recognition, by using the proposed methodology robots can calculate and estimate the rotational angles and pitch and roll angles of an object.