A Novel AVM Calibration Method Using Unaligned Square Calibration Boards
An around view monitoring (AVM) system acquires the front, rear, left, and right-side information of a vehicle using four cameras and transforms the four images into one image coordinate system to monitor around the vehicle with one image. Conventional AVM calibration utilizes the maximum likelihood...
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doaj-e307ddad949c4558bbb15fd7dd7491302021-03-25T00:01:31ZengMDPI AGSensors1424-82202021-03-01212265226510.3390/s21072265A Novel AVM Calibration Method Using Unaligned Square Calibration BoardsJung Hyun Lee0Dong-Wook Lee1Department of Electronics and Electrical Engineering, Dongguk University, Seoul 04620, KoreaDepartment of Electronics and Electrical Engineering, Dongguk University, Seoul 04620, KoreaAn around view monitoring (AVM) system acquires the front, rear, left, and right-side information of a vehicle using four cameras and transforms the four images into one image coordinate system to monitor around the vehicle with one image. Conventional AVM calibration utilizes the maximum likelihood estimation (MLE) to determine the parameters that can transform the captured four images into one AVM image. The MLE requires reference data of the image coordinate system and the world coordinate system to estimate these parameters. In conventional AVM calibration, many aligned calibration boards are placed around the vehicle and are measured to extract the reference sample data. However, accurately placing and measuring the calibration boards around a vehicle is an exhaustive procedure. To remediate this problem, we propose a novel AVM calibration method that requires only four randomly placed calibration boards by estimating the location of each calibration board. First, we define the AVM errors and determine the parameters that minimize the error in estimating the location. We then evaluate the accuracy of the proposed method through experiments using a real-sized vehicle and an electric vehicle for children to show that the proposed method can generate an AVM image similar to the conventional AVM calibration method regardless of a vehicle’s size.https://www.mdpi.com/1424-8220/21/7/2265around view monitoring systemautomatic camera calibrationvision-based advanced driver assistance systems |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jung Hyun Lee Dong-Wook Lee |
spellingShingle |
Jung Hyun Lee Dong-Wook Lee A Novel AVM Calibration Method Using Unaligned Square Calibration Boards Sensors around view monitoring system automatic camera calibration vision-based advanced driver assistance systems |
author_facet |
Jung Hyun Lee Dong-Wook Lee |
author_sort |
Jung Hyun Lee |
title |
A Novel AVM Calibration Method Using Unaligned Square Calibration Boards |
title_short |
A Novel AVM Calibration Method Using Unaligned Square Calibration Boards |
title_full |
A Novel AVM Calibration Method Using Unaligned Square Calibration Boards |
title_fullStr |
A Novel AVM Calibration Method Using Unaligned Square Calibration Boards |
title_full_unstemmed |
A Novel AVM Calibration Method Using Unaligned Square Calibration Boards |
title_sort |
novel avm calibration method using unaligned square calibration boards |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2021-03-01 |
description |
An around view monitoring (AVM) system acquires the front, rear, left, and right-side information of a vehicle using four cameras and transforms the four images into one image coordinate system to monitor around the vehicle with one image. Conventional AVM calibration utilizes the maximum likelihood estimation (MLE) to determine the parameters that can transform the captured four images into one AVM image. The MLE requires reference data of the image coordinate system and the world coordinate system to estimate these parameters. In conventional AVM calibration, many aligned calibration boards are placed around the vehicle and are measured to extract the reference sample data. However, accurately placing and measuring the calibration boards around a vehicle is an exhaustive procedure. To remediate this problem, we propose a novel AVM calibration method that requires only four randomly placed calibration boards by estimating the location of each calibration board. First, we define the AVM errors and determine the parameters that minimize the error in estimating the location. We then evaluate the accuracy of the proposed method through experiments using a real-sized vehicle and an electric vehicle for children to show that the proposed method can generate an AVM image similar to the conventional AVM calibration method regardless of a vehicle’s size. |
topic |
around view monitoring system automatic camera calibration vision-based advanced driver assistance systems |
url |
https://www.mdpi.com/1424-8220/21/7/2265 |
work_keys_str_mv |
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