Title
Deep Learning-Based Stair Segmentation And Behavioral Cloning For Autonomous Stair Climbing
Abstract
Mobile robots are widely used in the surveillance industry, for military and industrial applications. In order to carry out surveillance tasks like urban search and rescue operation, the ability to traverse stairs is of immense significance. This paper presents a deep learning-based approach for semantic segmentation of stairs, behavioral cloning for stair alignment, and a novel mechanical design for an autonomous stair climbing robot. The main objective is to solve the problem of locomotion over staircases with the proposed implementation. Alignment of a robot with stairs in an image is a traditional problem, and the most recent approaches are centered around hand-crafted texture-based Gabor filters and stair detection techniques. However, we could arrive at a more scalable and robust pipeline for alignment schemes. The proposed deep learning technique eliminates the need for manual tuning of parameters of the edge detector, the Hough accumulator and PID constants. The empirical results and architecture of stair alignment pipeline are demonstrated in this paper.
Year
DOI
Venue
2019
10.1142/S1793351X1940021X
INTERNATIONAL JOURNAL OF SEMANTIC COMPUTING
Keywords
DocType
Volume
Behavioral cloning, semantic segmentation, stair climbing robot
Journal
13
Issue
ISSN
Citations 
4
1793-351X
0
PageRank 
References 
Authors
0.34
0
7
Name
Order
Citations
PageRank
Navid Panchi100.34
Khush Agrawal200.34
Unmesh Patil300.68
Aniket Gujarathi400.68
Aman Jain501.01
Harsha Namdeo600.34
Shital S. Chiddarwar7578.91