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Sensors 2017, 17(6), 1341; doi:10.3390/s17061341

Convolutional Neural Network-Based Robot Navigation Using Uncalibrated Spherical Images

1
School of Computer Science and Engineering, Northwestern Polytechnical University, Xi’an 710072, China
2
Highly Automated Driving Team, HERE Technologies Automotive Division, Chicago, IL 60606, USA
This paper is an extended version of our paper published in Ran, L.; Zhang, Y.; Yang, T.; Zhang, P. Autonomous Wheeled Robot Navigation with Uncalibrated Spherical Images. In Chinese Conference on Intelligent Visual Surveillance; Springer: Singapore, 2016; pp. 47–55.
*
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Received: 23 February 2017 / Revised: 27 May 2017 / Accepted: 6 June 2017 / Published: 12 June 2017
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [3711 KB, uploaded 12 June 2017]   |  

Abstract

Vision-based mobile robot navigation is a vibrant area of research with numerous algorithms having been developed, the vast majority of which either belong to the scene-oriented simultaneous localization and mapping (SLAM) or fall into the category of robot-oriented lane-detection/trajectory tracking. These methods suffer from high computational cost and require stringent labelling and calibration efforts. To address these challenges, this paper proposes a lightweight robot navigation framework based purely on uncalibrated spherical images. To simplify the orientation estimation, path prediction and improve computational efficiency, the navigation problem is decomposed into a series of classification tasks. To mitigate the adverse effects of insufficient negative samples in the “navigation via classification” task, we introduce the spherical camera for scene capturing, which enables 360° fisheye panorama as training samples and generation of sufficient positive and negative heading directions. The classification is implemented as an end-to-end Convolutional Neural Network (CNN), trained on our proposed Spherical-Navi image dataset, whose category labels can be efficiently collected. This CNN is capable of predicting potential path directions with high confidence levels based on a single, uncalibrated spherical image. Experimental results demonstrate that the proposed framework outperforms competing ones in realistic applications. View Full-Text
Keywords: convolutional neural networks; vision-based robot navigation; spherical camera; navigation via learning convolutional neural networks; vision-based robot navigation; spherical camera; navigation via learning
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Ran, L.; Zhang, Y.; Zhang, Q.; Yang, T. Convolutional Neural Network-Based Robot Navigation Using Uncalibrated Spherical Images. Sensors 2017, 17, 1341.

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