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Deep Learning-Based Landmark Detection for Mobile Robot Outdoor Localization

1
Graduate School of Science and Engineering, Hosei University, 3-7-2 Kajinochō, Koganei, Tokyo 184-8584, Japan
2
Fairy Devices Inc., Tokyo 113-0034, Japan
3
Department of Electrical and Control Systems Engineering, National Institute of Technology, Toyama College, 13, Hongo-machi, Toyama 939-8045, Japan
4
Department of Mechanical Engineering, Hosei University, 3-7-2 Kajinochō, Koganei, Tokyo 184-8584, Japan
*
Author to whom correspondence should be addressed.
Machines 2019, 7(2), 25; https://doi.org/10.3390/machines7020025
Received: 28 February 2019 / Revised: 13 April 2019 / Accepted: 16 April 2019 / Published: 18 April 2019
Outdoor mobile robot applications generally implement Global Positioning Systems (GPS) for localization tasks. However, GPS accuracy in outdoor localization has less accuracy in different environmental conditions. This paper presents two outdoor localization methods based on deep learning and landmark detection. The first localization method is based on the Faster Regional-Convolutional Neural Network (Faster R-CNN) landmark detection in the captured image. Then, a feedforward neural network (FFNN) is trained to determine robot location coordinates and compass orientation from detected landmarks. The second localization employs a single convolutional neural network (CNN) to determine location and compass orientation from the whole image. The dataset consists of images, geolocation data and labeled bounding boxes to train and test two proposed localization methods. Results are illustrated with absolute errors from the comparisons between localization results and reference geolocation data in the dataset. The experimental results pointed both presented localization methods to be promising alternatives to GPS for outdoor localization. View Full-Text
Keywords: outdoor localization; deep learning; landmark detection; Faster R-CNN; CNN outdoor localization; deep learning; landmark detection; Faster R-CNN; CNN
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Nilwong, S.; Hossain, D.; Kaneko, S.-I.; Capi, G. Deep Learning-Based Landmark Detection for Mobile Robot Outdoor Localization. Machines 2019, 7, 25.

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