Computer-Vision Based Object Detection and Recognition for Service Robot in Indoor Environment

Abstract

The near future has been envisioned as a collaboration of humans with mobile robots to help in the day-to-day tasks. In this paper, we present a viable approach for a real-time computer vision based object detection and recognition for efficient indoor navigation of a mobile robot. The mobile robotic systems are utilized mainly for home assistance, emergency services and surveillance, in which critical action needs to be taken within a fraction of second or real-time. The object detection and recognition is enhanced with utilization of the proposed algorithm based on the modification of You Look Only Once (YOLO) algorithm, with lesser computational requirements and relatively smaller weight size of the network structure. The proposed computer-vision based algorithm has been compared with the other conventional object detection/recognition algorithms, in terms of mean Average Precision (mAP) score, mean inference time, weight size and false positive percentage. The presented framework also makes use of the result of efficient object detection/recognition, to aid the mobile robot navigate in an indoor environment with the utilization of the results produced by the proposed algorithm. The presented framework can be further utilized for a wide variety of applications involving indoor navigation robots for different services.