3D Distance Measurement from a Camera to a Mobile Vehicle, Using Monocular Vision

Estimation of distance from objects in real-world scenes is an important topic in several applications such as navigation of autonomous robots, simultaneous localization and mapping (SLAM), and augmented reality (AR). Even though there is a technology for this purpose, in some cases, this technology...

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Main Author: Saúl Martínez-Díaz
Format: Article
Language:English
Published: Hindawi Limited 2021-01-01
Series:Journal of Sensors
Online Access:http://dx.doi.org/10.1155/2021/5526931
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spelling doaj-8350e03f45774b6c9699ac1bb49414e52021-04-26T00:04:27ZengHindawi LimitedJournal of Sensors1687-72682021-01-01202110.1155/2021/55269313D Distance Measurement from a Camera to a Mobile Vehicle, Using Monocular VisionSaúl Martínez-Díaz0División de Estudios de Posgrado e InvestigaciónEstimation of distance from objects in real-world scenes is an important topic in several applications such as navigation of autonomous robots, simultaneous localization and mapping (SLAM), and augmented reality (AR). Even though there is a technology for this purpose, in some cases, this technology has some disadvantages. For example, GPS systems are susceptible to interference, especially in places surrounded by buildings, under bridges or indoors; alternatively, RGBD sensors can be used, but they are expensive, and their operational range is limited. Monocular vision is a low-cost suitable alternative that can be used indoor and outdoor. However, monocular odometry is challenging because the object location can be known up a scale factor. Moreover, when objects are moving, it is necessary to estimate the location from consecutive images accumulating error. This paper introduces a new method to compute the distance from a single image of the desired object, with known dimensions, captured with a monocular calibrated vision system. This method is less restrictive than other proposals in the state-of-the-art literature. For the detection of interest points, a Region-based Convolutional Neural Network combined with a corner detector were used. The proposed method was tested on a standard dataset and images acquired by a low-cost and low-resolution webcam, under noncontrolled conditions. The system was tested and compared with a calibrated stereo vision system. Results showed the similar performance of both systems, but the monocular system accomplished the task in less time.http://dx.doi.org/10.1155/2021/5526931
collection DOAJ
language English
format Article
sources DOAJ
author Saúl Martínez-Díaz
spellingShingle Saúl Martínez-Díaz
3D Distance Measurement from a Camera to a Mobile Vehicle, Using Monocular Vision
Journal of Sensors
author_facet Saúl Martínez-Díaz
author_sort Saúl Martínez-Díaz
title 3D Distance Measurement from a Camera to a Mobile Vehicle, Using Monocular Vision
title_short 3D Distance Measurement from a Camera to a Mobile Vehicle, Using Monocular Vision
title_full 3D Distance Measurement from a Camera to a Mobile Vehicle, Using Monocular Vision
title_fullStr 3D Distance Measurement from a Camera to a Mobile Vehicle, Using Monocular Vision
title_full_unstemmed 3D Distance Measurement from a Camera to a Mobile Vehicle, Using Monocular Vision
title_sort 3d distance measurement from a camera to a mobile vehicle, using monocular vision
publisher Hindawi Limited
series Journal of Sensors
issn 1687-7268
publishDate 2021-01-01
description Estimation of distance from objects in real-world scenes is an important topic in several applications such as navigation of autonomous robots, simultaneous localization and mapping (SLAM), and augmented reality (AR). Even though there is a technology for this purpose, in some cases, this technology has some disadvantages. For example, GPS systems are susceptible to interference, especially in places surrounded by buildings, under bridges or indoors; alternatively, RGBD sensors can be used, but they are expensive, and their operational range is limited. Monocular vision is a low-cost suitable alternative that can be used indoor and outdoor. However, monocular odometry is challenging because the object location can be known up a scale factor. Moreover, when objects are moving, it is necessary to estimate the location from consecutive images accumulating error. This paper introduces a new method to compute the distance from a single image of the desired object, with known dimensions, captured with a monocular calibrated vision system. This method is less restrictive than other proposals in the state-of-the-art literature. For the detection of interest points, a Region-based Convolutional Neural Network combined with a corner detector were used. The proposed method was tested on a standard dataset and images acquired by a low-cost and low-resolution webcam, under noncontrolled conditions. The system was tested and compared with a calibrated stereo vision system. Results showed the similar performance of both systems, but the monocular system accomplished the task in less time.
url http://dx.doi.org/10.1155/2021/5526931
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