Paper Title :Omnidirectional Monocular Distance Estimation Applicable to Autonomous Driving
Author :Yuki Ozawa, Shingo Kimura, Yiling Zhu, Atsutoshi Kurihara, Yue Bao
Article Citation :Yuki Ozawa ,Shingo Kimura ,Yiling Zhu ,Atsutoshi Kurihara ,Yue Bao ,
(2023 ) " Omnidirectional Monocular Distance Estimation Applicable to Autonomous Driving " ,
International Journal of Electrical, Electronics and Data Communication (IJEEDC) ,
pp. 11-18,
Volume-11,Issue-4
Abstract : This study proposes a 360° monocular distance estimation method using machine learning that can be applied to
autonomous driving. With the increasing development of autonomous driving technology in recent years, the private sector
has seen a rise in its usage, making it a familiar technology.In this context, sensors serve as the "eyes" of a machine and play
a crucial role in achieving higher performance at a lower cost.This study primarily focuses on depth measurement through
cameras, which offer a wide area and high density coverage.Depth estimation using cameras has been studied for a
significant period of time.Cameras are one of the essential sensors for recognizing the surrounding environment.Moreover,
the development of machine learning technology in recent years has significantly improved image recognition capabilities,
expanding the range of applications for cameras. Based on the above, we propose an omnidirectional monocular depth
estimation method involves the use ofour omnidirectional visual sensor and machine learning. our omnidirectional visual
sensor is a device that combines a hyperbolic mirror and a camera. By placing the center of the camera lens at the focus of
the hyperbolic mirror and taking a picture, an omnidirectional image can be captured. This image can be converted into a
panoramic image using image processing. Using this characteristic, our method captures an omnidirectional image through
the sensor and obtains a panoramic image through image processing. Then, depth estimation in all directions is performed by
monocular depth estimation using machine learning. However, the distance value obtained by monocular depth estimation
using machine learning is a relative value, so the actual distance could not be obtained. To overcome this limitation, our
study proposes a calibration method that estimates the absolute distance using regression curves.To validate our proposed
method, we created a prototype device and conducted three experiments, namely a depth image generation experiment, a
distance measurement experiment, and an absolute distance image generation experiment. In the depth image generation
experiment, the proposed method was able to generate accurate depth images with fewer detection omissions and obstacle
visibility when compared to the conventional method. In the distance measurement experiment, we demonstrated that our
proposed method can measure distances with an accuracy approaching that of the conventional method, even with a single
eye. The standard deviation was up to 29.1 cm lower and the resolution was up to 31.0cm higher than that of the
conventional method, indicating the possibility of highly stable measurement. In the absolute distance image generation
experiment, we showed that the use of regression curves can enable absolute distance estimation, overcoming the limitation
of depth estimation using machine learning based on relative values. As a result, the proposed method can provide accurate
and reliable measurements applicable to autonomous driving.
Keywords - Monocular Depth Estimation, Omnidirectional Camera, Distance Estimation, Autonomous Driving
Omnidirectional
Type : Research paper
Published : Volume-11,Issue-4
DOIONLINE NO - IJEEDC-IRAJ-DOIONLINE-19610
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Copyright: © Institute of Research and Journals
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Published on 2023-07-05 |
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