Preprint Article Version 1 This version is not peer-reviewed

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

Version 1 : Received: 24 December 2018 / Approved: 25 December 2018 / Online: 25 December 2018 (05:33:12 CET)

How to cite: Ilyas, M.; Lakshmanan, A.K.; Le, A.V.; Elara, M.R. Staircase Recognition and Localization Using Convolution Neural Network (CNN) for Cleaning Robot Application. Preprints 2018, 2018120296 (doi: 10.20944/preprints201812.0296.v1). Ilyas, M.; Lakshmanan, A.K.; Le, A.V.; Elara, M.R. Staircase Recognition and Localization Using Convolution Neural Network (CNN) for Cleaning Robot Application. Preprints 2018, 2018120296 (doi: 10.20944/preprints201812.0296.v1).

Abstract

Multi-floor environments are usually ignored while designing an autonomous robot for indoor cleaning applications. However, for efficient operation in such environments, the ability of a robotic platform to traverse staircases is crucial. Staircase detection and localization is highly important for planning the traversal on staircases. This paper describes a deep learning approach using Convolutional Neural Networks (CNNs) based Robot Operation System (ROS) to staircase detection and localization. We use an object detection network to detect staircases in 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 the angle of approach to the target point. Experiments are performed with data obtained from images captured on different types of staircases at different view points/angles. Results show that the approach is very accurate in identifying the presence of the staircase in the working environment and is also able to locate the target point with good accuracy.

Subject Areas

staircase recognition; Convolutional Neural Networks (CNN); re-configurable robot; contour detection

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