scholarly journals A Novel AVM Calibration Method Using Unaligned Square Calibration Boards

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2265
Author(s):  
Jung Hyun Lee ◽  
Dong-Wook Lee
Keyword(s):  
Coordinate System ◽  
Reference Data ◽  
Side Information ◽  
Reference Sample ◽  
Likelihood Estimation ◽  
Calibration Method ◽  
World Coordinate System ◽  
Sample Data ◽  
The World ◽  
Left And Right

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.

scholarly journals Structured Light 3D Reconstruction System Based on a Stereo Calibration Plate

Symmetry ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 772
Author(s):  
Meiying Li ◽  
Jin Liu ◽  
Haima Yang ◽  
Wanqing Song ◽  
Zihao Yu
Keyword(s):  
Coordinate System ◽  
Structured Light ◽  
Primary Objective ◽  
Calibration Data ◽  
Complex Objects ◽  
World Coordinate System ◽  
Calibration Methods ◽  
The World ◽  
Stereo Calibration ◽  
Left And Right

Calibration is a critical step in structured light 3D imaging systems. However, in the traditional calibration process, since the calibration plate is based on a two-dimensional model, the flatness of the calibration plate and the angle of the photo will affect the subsequent stitching steps based on the feature points. The number of photos also affects the calibration results. To improve the calibration accuracy, multiple photos need to be taken. The primary objective of this study was to achieve the simple and fast calibration of system parameters, so a method obtaining a large number of calibration data by homography matrix is presented, and a corresponding stereo target is designed in symmetry. First, using the relationship between the corner coordinates of the left and right parts of the stereo calibration plate and the coordinates of the world coordinate system, the homography matrix of the left and right calibration plates from the image coordinates to the world coordinates is calculated. Second, all the pixels in the stereo calibration plate are matched to the world coordinate system by using the homography matrix. In addition, we also compared the results of this method with those of traditional calibration methods. The experimental results show that the 3D geometric surface of the reconstruction result is smooth, it avoids the missing parts and the visual effect is excellent. Furthermore, the error range of small and complex objects can be reduced to 0.03 mm~0.05 mm. This method simplifies the calibration steps, reduces the calibration costs and has practical application value.

A Self-Calibration Method for Robotic Measurement System

1999 ◽  
Author(s):  
Chunhe Gong ◽  
Jingxia Yuan ◽  
Jun Ni
Keyword(s):  
Coordinate System ◽  
Measurement System ◽  
Calibration Method ◽  
Position Measurement ◽  
Robot Calibration ◽  
Robot System ◽  
Self Calibration ◽  
World Coordinate System ◽  
Robot Accuracy ◽  
Calibration Methods

Abstract Robot calibration plays an increasingly important role in manufacturing. For robot calibration on the manufacturing floor, it is desirable that the calibration technique be easy and convenient to implement. This paper presents a new self-calibration method to calibrate and compensate for robot system kinematic errors. Compared with the traditional calibration methods, this calibration method has several unique features. First, it is not necessary to apply an external measurement system to measure the robot end-effector position for the purpose of kinematic identification since the robot measurement system has a sensor as its integral part. Second, this self-calibration is based on distance measurement rather than absolute position measurement for kinematic identification; therefore the calibration of the transformation from the world coordinate system to the robot base coordinate system, known as base calibration, is not necessary. These features not only greatly facilitate the robot system calibration but also shorten the error propagation chain, therefore, increase the accuracy of parameter estimation. An integrated calibration system is designed to validate the effectiveness of this calibration method. Experimental results show that after calibration there is a significant improvement of robot accuracy over a typical robot workspace.

scholarly journals Refining Land-Cover Maps Based on Probabilistic Re-Classification in CCA Ordination Space

2020 ◽  
Vol 12 (18) ◽  
pp. 2954
Author(s):  
Yue Wan ◽  
Jingxiong Zhang ◽  
Wenjing Yang ◽  
Yunwei Tang
Keyword(s):  
Logistic Regression ◽  
Land Cover ◽  
Reference Data ◽  
Reference Sample ◽  
Training Sample ◽  
Small Sample ◽  
Cca Ordination ◽  
Sample Data ◽  
Map Data ◽  
Land Cover Maps

Due to spatial inhomogeneity of land-cover types and spectral confusions among them, land-cover maps suffer from misclassification errors. While much research has focused on improving image classification by re-processing source images with more advanced algorithms and/or using images of finer resolution, there is rarely any systematic work on re-processing existing maps to increase their accuracy. We propose refining existing maps to achieve accuracy gains by exploring and utilizing relationships between reference data, which are often already available or can be collected, and map data. For this, we make novel use of canonical correspondence analysis (CCA) to analyze reference-map class co-occurrences to facilitate probabilistic re-classification of map classes in CCA ordination space, a synthesized feature space constrained by map class occurrence patterns. Experiments using GlobeLand30 land-cover (2010) over Wuhan, China were carried out using reference sample data collected previously for accuracy assessment in the same area. Reference sample data were stratified by map classes and their spatial heterogeneity. To examine effects of model-training sample size on refinements, three subset samples (360, 720, and 1480 pixels) were selected from a pool of 3000 sample pixels (the full training sample). Logistic regression modeling was employed as a baseline method for comparisons. Performance evaluation was based on a test sample of 1020 pixels using a strict and relaxed definitions of agreement between reference classification and map classification, resulting in measures of types I and II, respectively. It was found that the CCA-based method is more accurate than logistic regression in general. With increasing sample sizes, refinements generally lead to greater accuracy gains. Heterogeneous sub-strata usually see greater accuracy gains than in homogeneous sub-strata. It was also revealed that accuracy gains in specific strata (map classes and sub-strata) are related to strata refinability. Regarding CCA-based refinements, a relatively small sample of 360 pixels achieved a 3% gain in both overall accuracy (OA) and F0.01 score (II). By using a selective strategy in which only refinable strata of cultivated land and forest are included in refinement, accuracy gains are further increased, with 5–11% gains in users’ accuracies (UAs) (II) and 4–10% gains in F0.01 scores (II). In conclusion, on condition of refinability, map refinement is well worth pursuing, as it increases accuracy of existing maps, extends utility of reference data, facilitates uncertainty-informed map representation, and enhances our understanding about relationships between reference data and map data and about their synthesis.

scholarly journals An Efficient Calibration Method for a Stereo Camera System with Heterogeneous Lenses Using an Embedded Checkerboard Pattern

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Pathum Rathnayaka ◽  
Seung-Hae Baek ◽  
Soon-Yong Park
Keyword(s):  
Calibration Method ◽  
Checkerboard Pattern ◽  
Camera System ◽  
Stereo Camera ◽  
Transformation Matrices ◽  
Narrow Angle ◽  
World Coordinate System ◽  
Left And Right ◽  
Image Pairs ◽  
The Right

We present two simple approaches to calibrate a stereo camera setup with heterogeneous lenses: a wide-angle fish-eye lens and a narrow-angle lens in left and right sides, respectively. Instead of using a conventional black-white checkerboard pattern, we design an embedded checkerboard pattern by combining two differently colored patterns. In both approaches, we split the captured stereo images into RGB channels and extract R and inverted G channels from left and right camera images, respectively. In our first approach, we consider the checkerboard pattern as the world coordinate system and calculate left and right transformation matrices corresponding to it. We use these two transformation matrices to estimate the relative pose of the right camera by multiplying the inversed left transformation with the right. In the second approach, we calculate a planar homography transformation to identify common object points in left-right image pairs and treat them with the well-known Zhangs camera calibration method. We analyze the robustness of these two approaches by comparing reprojection errors and image rectification results. Experimental results show that the second method is more accurate than the first one.

A Self-Calibration Method for Robotic Measurement System

1999 ◽  
Vol 122 (1) ◽  
pp. 174-181 ◽  
Author(s):  
Chunhe Gong ◽  
Jingxia Yuan ◽  
Jun Ni
Keyword(s):  
Coordinate System ◽  
Measurement System ◽  
Calibration Method ◽  
Position Measurement ◽  
Robot Calibration ◽  
Robot System ◽  
Self Calibration ◽  
World Coordinate System ◽  
Robot Accuracy ◽  
Calibration Methods

Robot calibration plays an increasingly important role in manufacturing. For robot calibration on the manufacturing floor, it is desirable that the calibration technique be easy and convenient to implement. This paper presents a new self-calibration method to calibrate and compensate for robot system kinematic errors. Compared with the traditional calibration methods, this calibration method has several unique features. First, it is not necessary to apply an external measurement system to measure the robot end-effector position for the purpose of kinematic identification since the robot measurement system has a sensor as its integral part. Second, this self-calibration is based on distance measurement rather than absolute position measurement for kinematic identification; therefore the calibration of the transformation from the world coordinate system to the robot base coordinate system, known as base calibration, is not necessary. These features not only greatly facilitate the robot system calibration, but also shorten the error propagation chain, therefore, increase the accuracy of parameter estimation. An integrated calibration system is designed to validate the effectiveness of this calibration method. Experimental results show that after calibration there is a significant improvement of robot accuracy over a typical robot workspace. [S1087-1357(00)01301-0]

scholarly journals Calibration Method of Orthogonally Splitting Imaging Pose Sensor Based on General Imaging Model

2018 ◽  
Vol 8 (8) ◽  
pp. 1399 ◽  
Author(s):  
Na Zhao ◽  
Changku Sun ◽  
Peng Wang
Keyword(s):  
Coordinate System ◽  
Radial Basis Function ◽  
Basis Function ◽  
Calibration Method ◽  
Calibration Procedure ◽  
Distortion Correction ◽  
Imaging Model ◽  
The World ◽  
Radial Basis ◽  
Pinhole Imaging

Orthogonally splitting imaging pose sensor is a new sensor with two orthogonal line array charge coupled devices (CCDs). Owing to its special structure, there are distortion correction and imaging model problems during the calibration procedure. This paper proposes a calibration method based on the general imaging model to solve these problems. The method introduces Plücker Coordinate to describe the mapping relation between the image coordinate system and the world coordinate system. This paper solves the mapping relation with radial basis function interpolation and adaptively selecting control points with Kmeans clustering method to improve the fitting accuracy. This paper determines the appropriate radial basis function and its shape parameter by experiments. And these parameters are used to calibrate the orthogonally splitting imaging pose sensor. According to the calibration result, the root mean square (RMS)of calibration dataset and the RMS of test dataset are 0.048 mm and 0.049 mm. A comparative experiment is conducted between the pinhole imaging model and the general imaging model. Experimental results show that the calibration method based on general imaging model applies to the orthogonally splitting imaging pose sensor. The calibration method requires only one image corresponding to the target in the world coordinates and distortion correction is not required to be taken into account. Compared with the calibration method based on the pinhole imaging model, the calibration procedure based on the general imaging model is easier and accuracy is greater.

Sign in / Sign up

Export Citation Format

Share Document