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Proceedings Paper

Vision-based mobile robot navigation through deep convolutional neural networks and end-to-end learning
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Paper Abstract

In contrast to humans, who use only visual information for navigation, many mobile robots use laser scanners and ultrasonic sensors along with vision cameras to navigate. This work proposes a vision-based robot control algorithm based on deep convolutional neural networks. We create a large 15-layer convolutional neural network learning system and achieve the advanced recognition performance. Our system is trained from end to end to map raw input images to direction in supervised mode. The images of data sets are collected in a wide variety of weather conditions and lighting conditions. Besides, the data sets are augmented by adding Gaussian noise and Salt-and-pepper noise to avoid overfitting. The algorithm is verified by two experiments, which are line tracking and obstacle avoidance. The line tracking experiment is proceeded in order to track the desired path which is composed of straight and curved lines. The goal of obstacle avoidance experiment is to avoid the obstacles indoor. Finally, we get 3.29% error rate on the training set and 5.1% error rate on the test set in the line tracking experiment, 1.8% error rate on the training set and less than 5% error rate on the test set in the obstacle avoidance experiment. During the actual test, the robot can follow the runway centerline outdoor and avoid the obstacle in the room accurately. The result confirms the effectiveness of the algorithm and our improvement in the network structure and train parameters

Paper Details

Date Published: 19 September 2017
PDF: 8 pages
Proc. SPIE 10396, Applications of Digital Image Processing XL, 1039622 (19 September 2017); doi: 10.1117/12.2272648
Show Author Affiliations
Yachu Zhang, Beijing Institute of Technology (China)
Yuejin Zhao, Beijing Institute of Technology (China)
Ming Liu, Beijing Institute of Technology (China)
Liquan Dong, Beijing Institute of Technology (China)
Lingqin Kong, Beijing Institute of Technology (China)
Lingling Liu, Beijing Institute of Technology (China)


Published in SPIE Proceedings Vol. 10396:
Applications of Digital Image Processing XL
Andrew G. Tescher, Editor(s)

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