Real-time deep learning-based image processing for pose estimation and object localization in autonomous robot applications

Artificial intelligence (AI) is shaping manufacturing to make it smarter, intelligent, and autonomous. Presently, flexible robots have been introduced that collaborate with humans on the shop floor to enhance productivity and efficiency. Object classification and pose estimation in an autonomous robotic system are crucial problems for proper grasping. Extensive research is being conducted to achieve low-cost, computationally efficient, and real-time assessments. However, most of the existing approaches are computationally expensive and constrained to previous knowledge of the 3D structure of an object. This article presents an AI-based solution, which generalizes cuboid- and cylindrical-shaped objects' grasping in real-time, irrespective of the dimensions. The AI algorithm has achieved an average precision of 89.44% and 82.43% for cuboid- and cylindrical-shaped objects. It is identified without the knowledge of the objects' 3D model. The pose is estimated in real-time, accurately. The integrated solution has been implemented in a robotic system fitted with two grippers, a conveyor system, and sensors. Results of several experiments have been reported in this article, which validates the solution. The proposed methodology has achieved 100% accuracy during our experiments to grasp objects on the conveyor belt.