Convolutional Neural Network-Based Robot Navigation Using Uncalibrated Spherical Images

作     者：Ran, Lingyan Zhang, Yanning Zhang, Qilin Yang, Tao 

作者机构：Northwestern Polytech Univ Sch Comp Sci & Engn Xian 710072 Peoples R China HERE Technol Automot Div Highly Automated Driving Team Chicago IL 60606 USA 

出 版 物：《SENSORS》 (传感器)

年 卷 期：2017年第17卷第6期

页      面：1341-1341页

核心收录：

学科分类：0710[理学-生物学] 071010[理学-生物化学与分子生物学] 0808[工学-电气工程] 07[理学] 0804[工学-仪器科学与技术] 0703[理学-化学] 

基　　金：National Natural Science Foundation of China [61672429, 61272288, 61231016] National High Technology Research and Development Program of China (863 Program) [2015AA016402] ShenZhen Science and Technology Foundation [JCYJ20160229172932237] Northwestern Polytechnical University (NPU) New AoXiang Star [G2015KY0301] Fundamental Research Funds for the Central Universities [3102015AX007] NPU New People and Direction [13GH014604] 

主　　题：convolutional neural networks vision-based robot navigation spherical camera navigation via learning 

摘      要：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 degrees 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.