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| Content Provider | MDPI |
|---|---|
| Author | Ran, Lingyan Zhang, Yanning Zhang, Qilin Yang, Tao |
| 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. |
| File Size | 3800064 |
| File Format | |
| e-ISSN | 14248220 |
| DOI | 10.3390/s17061341 |
| Journal | Sensors |
| Issue Number | 6 |
| Volume Number | 17 |
| Language | English |
| Publisher Date | 2017-06-12 |
| Access Restriction | Open |
| Subject Keyword | convolutional neural networks vision-based robot navigation spherical camera navigation via learning |
| Content Type | Text |
| Resource Type | Article |
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