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Sensors 2018, 18(1), 177; https://doi.org/10.3390/s18010177

Small Imaging Depth LIDAR and DCNN-Based Localization for Automated Guided Vehicle

Department of System & Electronics Engineering, Toyota Central R&D Labs., Inc., 41-1, Yokomichi, Nagakute, Aichi 480-1192, Japan
This paper is an extended version of our paper published in Ito, S.; Hiratsuka, S.; Ohta, M.; Matsubara, H.; Ogawa, M. SPAD DCNN: Localization with Small Imaging LIDAR and DCNN. In Proceedings of the IEEE International Workshop on Intelligent Robots and Systems (IROS), Vancouver, BC, Canada, 24–28 September 2017.
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Received: 1 November 2017 / Revised: 20 December 2017 / Accepted: 21 December 2017 / Published: 10 January 2018
(This article belongs to the Special Issue Imaging Depth Sensors—Sensors, Algorithms and Applications)
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Abstract

We present our third prototype sensor and a localization method for Automated Guided Vehicles (AGVs), for which small imaging LIght Detection and Ranging (LIDAR) and fusion-based localization are fundamentally important. Our small imaging LIDAR, named the Single-Photon Avalanche Diode (SPAD) LIDAR, uses a time-of-flight method and SPAD arrays. A SPAD is a highly sensitive photodetector capable of detecting at the single-photon level, and the SPAD LIDAR has two SPAD arrays on the same chip for detection of laser light and environmental light. Therefore, the SPAD LIDAR simultaneously outputs range image data and monocular image data with the same coordinate system and does not require external calibration among outputs. As AGVs travel both indoors and outdoors with vibration, this calibration-less structure is particularly useful for AGV applications. We also introduce a fusion-based localization method, named SPAD DCNN, which uses the SPAD LIDAR and employs a Deep Convolutional Neural Network (DCNN). SPAD DCNN can fuse the outputs of the SPAD LIDAR: range image data, monocular image data and peak intensity image data. The SPAD DCNN has two outputs: the regression result of the position of the SPAD LIDAR and the classification result of the existence of a target to be approached. Our third prototype sensor and the localization method are evaluated in an indoor environment by assuming various AGV trajectories. The results show that the sensor and localization method improve the localization accuracy. View Full-Text
Keywords: single-photon avalanche diode; light detection and ranging; imaging LIDAR; deep learning; deep convolutional neural network; localization; sensor fusion single-photon avalanche diode; light detection and ranging; imaging LIDAR; deep learning; deep convolutional neural network; localization; sensor fusion
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Ito, S.; Hiratsuka, S.; Ohta, M.; Matsubara, H.; Ogawa, M. Small Imaging Depth LIDAR and DCNN-Based Localization for Automated Guided Vehicle. Sensors 2018, 18, 177.

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