Manuscript received September 6, 2023; revised September 30, 2023; accepted October 15 2023; published January 4, 2024.

Abstract—Delivering robots impact many facets of our life, including food delivery and restaurant services, with advancements enabling obstacle overcome, faster delivery, and minimizing human intervention. However, delivering robots remained to experience poor vertical mobility-elevator usage in multi-floor buildings. Incorporating new elevator models into the robot’s elevator usage capabilities involves a long process of manufacturer approval and authentication. Furthermore, strict fire-code regulations pose communication barriers between the robot and the elevator. In this paper, we introduce MirrorVision-a novel approach designed for accurate floor detection during vertical mobility, regardless of obstructions blocking the robot’s direct line of sight to the elevator number panel. First, we collected and pre-processed a dataset of direct and reflective views of elevator number panels via the pre-installed mirrors. Then, we trained mirrored images in various possibilities to accomplish accurate floor detection. MirrorVision provides a solid mechanism to understand floor numbers at the level of distorted images. Extensive evaluations show that MirrorVision achieves 98.8% accuracy for floor detection in a crowded elevator, while state-of-the-art EfficientDet and YOLOv5 achieved 90.8% and 93.3%, respectively.

Keywords—autonomous robots, floor detection, indoor navigation, MirrorVision, faster Region Convolution Neural Network (R-CNN), EfficientDet, YOLOv5

Cite: Azimbek Khudoyberdiev and Jihoon Ryoo, "MirrorVision: Light-Weight Floor Detection System for an Autonomous Robot in a Crowded Elevator," Journal of Image and Graphics, Vol. 12, No. 1, pp. 1-9, 2024.

Copyright © 2024 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.