3D Object Detection and Recognition based on RGBD Images for Healthcare Robot

Lately, during the COVID-19 pandemic, hospitals experienced an increase in the number of patients due to the rapid spread of the virus. The need for services in hospitals has increased compared to normal days. Therefore Healthcare Robot is needed that can help the service of patients and medical personnel in the hospital. The robot must be able to detect and recognize existing objects and put them in the expected place. The sensor itself here uses a camera depth or stereo camera. The input results are in the form of RGB-D Image, which we then convert to point cloud to get 3D information. Then the 3D information will be segmented and clustered to get the object to be detected using a RANSAC and Euclidean Cluster. Then feature extraction uses the Viewpoint Features Histogram (VFH) descriptor to get the characteristics of the object. Then the matching with the dataset using the Artificial Neural Network continued with Labelling and visualization of the result. With this system, the robot can detect and recognize objects around the hospital so that the robot can take action on these objects. At the end of this project, nine datasets and three scenes resulting from capture by the writer were tested. The results show an average accuracy of 90.77% for testing three multi-object scenes and 98.73% for testing one object.