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Quality Index of Supervised Data for Convolutional Neural Network-Based Localization

Department of System and Electronics Engineering, Toyota Central R&D Labs., Inc., 41-1, Yokomichi, Nagakute, Aichi 480-1192, Japan
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Appl. Sci. 2019, 9(10), 1983; https://doi.org/10.3390/app9101983
Received: 4 February 2019 / Revised: 26 April 2019 / Accepted: 8 May 2019 / Published: 15 May 2019
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Abstract

Automated guided vehicles (AGVs) are important in modern factories. The main functions of an AGV are its own localization and object detection, for which both sensor and localization methods are crucial. For localization, we used a small imaging sensor named a single-photon avalanche diode (SPAD) light detection and ranging (LiDAR), which uses the time-of-flight principle and arrays of SPADs. The SPAD LiDAR works both indoors and outdoors and is suitable for AGV applications. We utilized a deep convolutional neural network (CNN) as a localization method. For accurate CNN-based localization, the quality of the supervised data is important. The localization results can be poor or good if the supervised training data are noisy or clean, respectively. To address this issue, we propose a quality index for supervised data based on correlations between consecutive frames visualizing the important pixels for CNN-based localization. First, the important pixels for CNN-based localization are determined, and the quality index of supervised data is defined based on differences in these pixels. We evaluated the quality index in indoor-environment localization using the SPAD LiDAR and compared the localization performance. Our results demonstrate that the index correlates well to the quality of supervised training data for CNN-based localization. View Full-Text
Keywords: deep learning; autonomous intelligent robots; LiDAR; localization deep learning; autonomous intelligent robots; LiDAR; localization
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Ito, S.; Soga, M.; Hiratsuka, S.; Matsubara, H.; Ogawa, M. Quality Index of Supervised Data for Convolutional Neural Network-Based Localization. Appl. Sci. 2019, 9, 1983.

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