Indoor Room Identify and Mapping with Virtual based SLAM using Furnitures and Household Objects Relationship based on CNNs

In order to make autonomous home service robot able to navigate through its environment, one requires a surrounding map and the robot's location. The Simultaneous Localization And Mapping or SLAM is the method that gathers information from an interested unknown environment, and creates a map and also predicts robot position at the same time. SLAM map is not enough for robot builder companies to sell their service robot because the robots cannot recognize the room in house without complex setup from the experts. The robot cannot be opened from its package and immediately ready to be used. In this research, one method to overcome this issue is proposed by enhancing SLAM algorithm with furniture and household object detection CNN network in order to increase robot ability. Robot will create maps by using a Laser Scan Matcher based on visual SLAM using 3D Orbbec camera. YOLO v3 tiny network is selected as the CNN detector for localize and classify household objects and furnitures in a house. Furnitures and objects images are used to train the CNN networks separately in desktop PC and are installed into the robot after training is finished. CNN detector is combined with SLAM algorithm via ROS. Now, SLAM map can be generated and room can be detected simultaneously in the unknown environment automatically. Finally, experiment is conducted to test the proposed method.