A Concise Dataset for Intelligent Behaviors in Domestic Tasks

Abstract

For service robots operating in domestic environments, high-level intelligent behaviors require a comprehensive understanding of objects through visual perception. The random placement of objects introduces variations that impact the accuracy of object detection and recognition. This study presents a novel method for automatically generating a concise image dataset, named the Object Dataset Federal University of Technology (ODUTF), to enhance intelligent behaviors in service robots to focus on domestic tasks. The dataset is produced using an automatic multicapture device that gathers RGB images, stereo information, depth images, and point-cloud data. This device has two degrees of freedom to adjust both the orientation of objects and the camera’s viewpoint. The method creates a precise and detailed visual description of objects, which improves a service robot’s ability to approach and pick up objects. This approach is evaluated within the context of the RoboCup@Home Brazil League, part of the international RoboCup competition dedicated to domestic service robots. This league involves diverse tasks, emphasizing object detection and recognition. The use of high-level intelligent behaviors is critical for overcoming domestic challenges, and ODUTF facilitates the deployment of more reliable deep neural network methods for tracking objects during pick-up tasks. Furthermore, ODUTF can be dynamically adapted using post-processing scripts to incorporate artificial features like varying backgrounds, luminosity, and noise.