Staircase Recognition and Localization Using Convolutional Neural Network (CNN) for Cleaning Robot Application

Muhammad Ilyas (), Anirudh Krishna Lakshmanan, Anh Vu Le and Mohan Rajesh Elara ()
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Muhammad Ilyas: School of IT and Engineering, Kazakh-British Technical University (KBTU), Almaty 050000, Kazakhstan
Anirudh Krishna Lakshmanan: Engineering Product Development Pillar, Singapore University of Technology and Design (SUTD), Singapore 487372, Singapore
Anh Vu Le: Communication and Signal Processing Research Group, Faculty of Electrical and Electronics Engineering, Ton Duc Thang University, Ho Chi Minh City 700000, Vietnam
Mohan Rajesh Elara: Engineering Product Development Pillar, Singapore University of Technology and Design (SUTD), Singapore 487372, Singapore

Mathematics, 2023, vol. 11, issue 18, 1-19

Abstract: Floor-cleaning robots are primarily designed to clean on a single floor, while multi-floor environments are usually not considered target applications. However, it is more efficient to have an autonomous floor-cleaning robot that can climb stairs and reach the next floors in a multi-floor building. To operate in such environments, the ability of a mobile robot to autonomously traverse staircases is very important. For this operation, staircase detection and localization are essential components for planning the traversal route on staircases. This article describes a deep learning approach using a convolutional neural network (CNN)-based robot operation system (ROS) framework for staircase detection, localization, and maneuvering of the robot to the detected stair. We present a real-time object detection framework to detect staircases in incoming images. We also localize these staircases using a contour detection algorithm to detect the target point: a point close to the center of the first step, and an angle of approach to the target point with respect to the current location of the robot. Experiments are performed with data from images captured on different types of staircases at different viewpoints/angles. The experimental results show that the presented approach can achieve an accuracy of 95% and a recall of 86.81%. A total runtime of 155 ms is taken to identify the presence of a staircase and the detection of the first step in the working environment, as well as being able to locate the target point with an accuracy of ±2 cm, ±1 degree.

Keywords: staircase recognition; convolutional neural networks (CNNs); re-configurable robot; contour detection (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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