Auto-Calibration Around-View Monitoring System

Chang, Yu-Lung; Hsu, Li-You; Chen, Oscal T.‐C.

doi:10.1109/vtcfall.2013.6692111

Cited by 15 publications

(8 citation statements)

References 6 publications

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“…However, the methods in this approach are quite inconvenient, because drivers must prepare those patterns themselves, or visit repair shops that can equip those patterns. Chang et al [ 5 ] utilized a pattern composed of multiple rectangles drawn on the ground. This method compares images of rectangles with the reference rectangles to estimate extrinsic parameters of four cameras composing the AVM system.…”

Section: Related Researchmentioning

confidence: 99%

Automatic Calibration of an Around View Monitor System Exploiting Lane Markings

Choi

Jung

Suhr

2018

Sensors

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This paper proposes a method that automatically calibrates four cameras of an around view monitor (AVM) system in a natural driving situation. The proposed method estimates orientation angles of four cameras composing the AVM system, and assumes that their locations and intrinsic parameters are known in advance. This method utilizes lane markings because they exist in almost all on-road situations and appear across images of adjacent cameras. It starts by detecting lane markings from images captured by four cameras of the AVM system in a cost-effective manner. False lane markings are rejected by analyzing the statistical properties of the detected lane markings. Once the correct lane markings are sufficiently gathered, this method first calibrates the front and rear cameras, and then calibrates the left and right cameras with the help of the calibration results of the front and rear cameras. This two-step approach is essential because side cameras cannot be fully calibrated by themselves, due to insufficient lane marking information. After this initial calibration, this method collects corresponding lane markings appearing across images of adjacent cameras and simultaneously refines the initial calibration results of four cameras to obtain seamless AVM images. In the case of a long image sequence, this method conducts the camera calibration multiple times, and then selects the medoid as the final result to reduce computational resources and dependency on a specific place. In the experiment, the proposed method was quantitatively and qualitatively evaluated in various real driving situations and showed promising results.

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Section: Related Researchmentioning

confidence: 99%

Automatic Calibration of an Around View Monitor System Exploiting Lane Markings

Choi

Jung

Suhr

2018

Sensors

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“…Vehicle man ufacturers use AVM calibration facilities to measure the calibration board locations ac curately, as shown in Figure 2 [7]. Various AVM calibration studies are also based o well-aligned calibration boards [8][9][10][11][12][13][14][15][16][17][18][19]. The details of these studies are provided in Sec tion 2.1.…”

Section: Introductionmentioning

confidence: 99%

A Novel AVM Calibration Method Using Unaligned Square Calibration Boards

Lee

2021

Sensors

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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.

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“…In this study, we focus on an AVM system, which comprises cameras in the front, rear, left, and right of the vehicle, respectively, to display the 360°view of the surroundings of the vehicle. Additionally, we focus on constructing a system that assists in parking by synthesizing images from the cameras to from a topview image [1][2][8][9][10][11][12][13]. However, AVM cameras must be calibrated to synthesize top-view images.…”

Section: Introductionmentioning

confidence: 99%

An Automatic Calibration Method for AVM Cameras

Lee

Kim

2020

IEEE Access

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We introduce a method for the efficient calibration of around-view-monitoring (AVM) cameras. Particularly, we introduce two situations that require calibration because of the characteristics of AVM cameras: a situation wherein cameras are shipped from the manufacturing line and another situation wherein some cameras are distorted during operation and need recalibration. In this study, the calibration method for shipped cameras is defined as the factory mode and that for recalibration is defined as the service mode. In the factory mode, two circular patterns placed at a regular distance are used to ensure the maximum accuracy while requiring minimum calibration. In the service mode, as a recalibration method, we developed a robust method that considers various environments using parallel parking lines that can be easily installed in general service centers. In the factory mode, we confirmed that the AVM-camera-calibration error was within 5.66 cm when the two circular patterns were located at a certain distance within a certain range. However, in the service mode, we achieved camera movement angle error equal to or less than 0.1°using the parking-line-detection result.

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Auto-Calibration Around-View Monitoring System

Cited by 15 publications

References 6 publications

Automatic Calibration of an Around View Monitor System Exploiting Lane Markings

Automatic Calibration of an Around View Monitor System Exploiting Lane Markings

A Novel AVM Calibration Method Using Unaligned Square Calibration Boards

An Automatic Calibration Method for AVM Cameras

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