Convolutional Neural Network-Based Robot Navigation Using Uncalibrated Spherical Images

• Published in: Sensors (Basel, Switzerland) Vol. 17; no. 6; p. 1341
• Main Authors: Ran, Lingyan, Zhang, Yanning, Zhang, Qilin, Yang, Tao
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 12.06.2017; MDPI
• Subjects: convolutional neural networks; navigation via learning; Neural networks; Robots; spherical camera; vision-based robot navigation; navigation via learning; vision-based robot navigation; convolutional neural networks; spherical camera
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s17061341

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.