Development of small size 3D LIDAR

Kimoto, Kôichi; Asada, Norihiro; Mori, Toshihiro; Hara, Yoshitaka; Ohya, Akihisa; Yuta, Shin’ichi

doi:10.1109/icra.2014.6907534

Cited by 47 publications

(31 citation statements)

References 4 publications

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“…Therefore, in this study, we attempted to develop long-range 3D imaging LIDAR with fusion-based localization. 3D LIDAR [ 1 , 2 ] has become popular for use in AGV and autonomous car applications. In the Defense Advanced Research Projects Agency (DARPA) Urban Challenge, many teams [ 18 , 19 , 20 ] used the Velodyne HDL-64 [ 1 ] LIDAR, which has 64 laser transmitters.…”

Section: Related Workmentioning

confidence: 99%

“…However, VLP-16 still has many laser transmitters and receivers. In contrast, the small 3D LIDAR proposed by Kimoto [ 2 ] uses a single pair of sensors consisting of a laser transmitter and receiver. That LIDAR mechanically rotates a resonant mirror and reconstructs 8000 points at 20 Hz.…”

Section: Related Workmentioning

confidence: 99%

“…LIDAR [ 1 , 2 ] is necessary to support the capabilities of AGVs and autonomous cars. An extremely popular LIDAR product for these vehicles is that produced by Velodyne [ 1 ].…”

Section: Introductionmentioning

confidence: 99%

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

Itô

Hiratsuka

Ohta

et al. 2018

Sensors

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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.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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

Itô

Hiratsuka

Ohta

et al. 2018

Sensors

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“…The LiDAR is able to operate normally in cloud cover, fog, haze, or rain, during the day and night [21][22][23][24][25][26], and has been miniaturized sufficiently to allow it to be installed in a small-size drone or boat [27,28]. The detected data is also intuitive upon viewing such that that expert interpretation is not required [29][30][31][32][33][34][35][36].…”

Section: Introductionmentioning

confidence: 99%

Geometrical Properties of Spilled Oil on Seawater Detected Using a LiDAR Sensor

Moon¹,

Jung²

2020

Journal of Sensors

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We report on a small-size light detection and ranging (LiDAR) sensor, which offers the possibility of being used in the field during oil spill incidents. In the present study, we develop an algorithm that can distinguish between seawater and oil through the use of a laser at 905 nm wavelength. We investigate the ability of the sensor to detect three different oil types (light crude, bunker A, and bunker C) through experiments and analyze the differences between the types and volumes of spilled oil (1, 5, 10, 15, 20, 25, 30, 35, 40, and 50 mL). The results showed that our algorithm for detecting oil spills over seawater was successful: the LiDAR sensor was able to detect different oil types and volumes. Spilled oil area coverage ranged by more than 50% of the detected area, and the viscosity of bunker C oil reached up to 73%. In addition, the experimental oil spills were mainly formed of oil films of 1 mm and 2 mm thicknesses, which confirmed geometrical properties. Follow-up research should further investigate the characteristics of oil slick thickness measured by the LiDAR system and undertake field tests to assess the feasibility of using the LiDAR system in pollution incidents.

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“…Motivated by this, an ad-hoc solution for obtaining a low-cost 3D range sensor would involve rotating a 2D lidar device around its axes. Fortunately, this solution is frequently used in the literature (Kang and Doh, 2016; Ohno et al, 2009; Surmann et al, 2001), including some other techniques, such as using a spring-mounted structure (Bosse et al, 2012) or a resonant mirror on a scanning motor (Kimoto et al, 2014) to generate 3D range information with a single 2D lidar device. Surmann et al (2001) use a standard servomotor to rotate a 2D lidar device attached to an extended mount around a horizontal axis.…”

Section: Introductionmentioning

confidence: 99%

Enhancing 3D range image measurement density via dynamic Papoulis–Gerchberg algorithm

Kuzucu

Öztürk

Gül

et al. 2018

Transactions of the Institute of Measurement and Control

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As one of the most popular range detection methods, lidar is commonly used in various robotic applications. Although most robotic platforms easily adopt 2D lidar for range sensing, 3D lidar is rarely used in mobile robots, owing to its high cost. Some methods reported in the literature obtain 3D range information by rotating a single 2D lidar device. However, for most of these methods, there is a trade-off between 3D scan frequency and measurement density. Existing methods discussed in the literature for increasing the measurement density in high-frequency lidar have high time complexity and require certain conditions on data distribution. In a previous work, we showed the usability of an image super-resolution method, the Papoulis–Gerchberg (P–G) algorithm, on range data represented in the form of a greyscale image. However, the low convergence rate of the original P–G algorithm impedes its use for online applications. In this study, we advanced the P–G algorithm to drastically reduce the convergence time and improve performance by utilizing previous range images. The proposed algorithm now supports application on a mobile robot with online measurement density enhancement for 3D range images collected by rotating a 2D lidar device around its pitch axis with a high 3D scan frequency. We show illustrative examples for different scenarios to present the effectiveness of the proposed method on a 3D range sensor mounted on a mobile robot.

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Development of small size 3D LIDAR

Cited by 47 publications

References 4 publications

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

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

Geometrical Properties of Spilled Oil on Seawater Detected Using a LiDAR Sensor

Enhancing 3D range image measurement density via dynamic Papoulis–Gerchberg algorithm

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