Rectification Using Different Types of Cameras Attached to a Vehicle

Dinh, Vinh Quang; Nguyên, Tiên Hiêp; Jeon, Jae Wook

doi:10.1109/tip.2018.2870930

Cited by 15 publications

(8 citation statements)

References 21 publications

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“…To continue the experiment, we need all the multiple pairs of transformations 2T 1 , 3T 1 , 2T 3 , etc., which are calculated by simple algebraic manipulation: for example, 2T 1 = 0T 2 −1 0T 1 , and so on. As it is expected, with these "perfect" geometric transformations, all the procedures presented earlier, namely, the average transformation given by expression (15) and related, produce the perfect result. In other words, the following operations,…”

Section: Propagation Of Uncertainty With a Simulated Experimentssupporting

confidence: 53%

“…This pairwise approach must consider all possible combinations between sensor modalities in the pair. These sensor combinations have been addressed in the literature: RGB to RGB camera calibration [1,[11][12][13][14][15], RGB to depth camera (RGB-D cameras) calibration [9,[16][17][18][19][20], camera to 2D LIDAR [1,4,19,[21][22][23][24][25], 2D LIDAR to 3D LIDAR [10], camera to 3D LIDAR [25][26][27], and camera to radar [28].…”

Section: Related Workmentioning

confidence: 99%

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Multi-Sensor Extrinsic Calibration Using an Extended Set of Pairwise Geometric Transformations

Santos

Rato

Dias

et al. 2020

Sensors

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Systems composed of multiple sensors for exteroceptive perception are becoming increasingly common, such as mobile robots or highly monitored spaces. However, to combine and fuse those sensors to create a larger and more robust representation of the perceived scene, the sensors need to be properly registered among them, that is, all relative geometric transformations must be known. This calibration procedure is challenging as, traditionally, human intervention is required in variate extents. This paper proposes a nearly automatic method where the best set of geometric transformations among any number of sensors is obtained by processing and combining the individual pairwise transformations obtained from an experimental method. Besides eliminating some experimental outliers with a standard criterion, the method exploits the possibility of obtaining better geometric transformations between all pairs of sensors by combining them within some restrictions to obtain a more precise transformation, and thus a better calibration. Although other data sources are possible, in this approach, 3D point clouds are obtained by each sensor, which correspond to the successive centers of a moving ball its field of view. The method can be applied to any sensors able to detect the ball and the 3D position of its center, namely, LIDARs, mono cameras (visual or infrared), stereo cameras, and TOF cameras. Results demonstrate that calibration is improved when compared to methods in previous works that do not address the outliers problem and, depending on the context, as explained in the results section, the multi-pairwise technique can be used in two different methodologies to reduce uncertainty in the calibration process.

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Section: Propagation Of Uncertainty With a Simulated Experimentssupporting

confidence: 53%

Section: Related Workmentioning

confidence: 99%

Multi-Sensor Extrinsic Calibration Using an Extended Set of Pairwise Geometric Transformations

Santos

Rato

Dias

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…Unlike the original rectification method [21], [26], the algorithms are extended to look for the nine optimized parameters [22], [29]: ( θ ly θ lz θ rx θ ry θ rz g l g r t l t r ). Instead of using the same focal length f for both left and right images, the authors used different values of f for each images, resulting in different values for K l and K r .…”

Section: B Uncalibrated Rectification With Low Geometric Distortionmentioning

confidence: 99%

“…However, their approach needs focal length information [24] or a calibration process [25]. Dinh et al [26] presented the first algorithm that estimated a re-scaling ratio using correspondence information by including a FoV compensation module. Their method is inefficient because it requires repeating scale-invariant feature transform (SIFT) procedure [27].…”

Section: Introductionmentioning

confidence: 99%

Accurate Uncalibrated Rectification Using Feature Matching Re-Selection

Tran

Jeon

2021

IEEE Access

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Rectification is a core programming process that involves multiple views. In this study, we focus on uncalibrated cases that neglect intrinsic and extrinsic camera information. Existing uncalibrated rectification methods use feature-matching techniques to form relationships between different views and use those features to estimate optimized homography matrices. However, outliers are inevitable in feature matching. Using these outliers in a rectification process produces vertical disparity errors and unwanted geometric distortion. To tackle the problem, we propose a novel method that can learn from the rectification results, re-select the matching pairs, and find the superior solutions. The proposed method introduces a novel workflow for uncalibrated rectification that incorporates three cores: field of view (FoV) neutralization, rectification, and feature matching re-selection (FMR). While the FoV neutralization module handles FoV differences among views, a combination of the rectification module and FMR module results in the optimal homography matrices. The rectification module takes neutral correspondences and estimates the optimized rectified matrices. Applying the results from the rectification module, the FMR module optimizes the correspondences and return them to the rectification module. For the rectification module, we apply adaptive geometric constraints and our updated optimization strategy to secure the satisfied vertical disparity errors while maintaining low distortion levels. Multidisciplinary experiments are performed to analyze the capabilities of the proposed method. We combine existing datasets with extra samples, including various outdoor environments, to gauge the performance better. Compared with existing methods, the proposed method produces fewer rectification errors and keeps the rectified images under low distortion rates.

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“…It removes parallax in the vertical direction of images after the rectification process that aligns epipolar lines in calibrated left and right images [11]. The parallax of the right image that reflects the left image is measured using the rectified left and right images, and depth information is extracted [12,13].…”

Section: Introductionmentioning

confidence: 99%

Image Distortion and Rectification Calibration Algorithms and Validation Technique for a Stereo Camera

2021

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This paper focuses on the calibration problem using stereo camera images. Currently, advanced vehicle systems such as smart cars and mobile robots require accurate and reliable vision in order to detect obstacles and special marks around. Such modern vehicles can be equipped with sensors and cameras together or separately. In this study, we propose new methodologies of stereo camera calibration based on the correction of distortion and image rectification. Once the calibration is complete, the validation of the corrections is presented followed by an evaluation of the calibration process. Usually, the validation section is not jointly considered with the calibration in other studies. However, the mass production of cameras widely uses the validation techniques in calibrations owned by manufacturing businesses. Here, we aim to present a single process for the calibration and validation of stereo cameras. The experiment results showed the disparity maps in comparison with another study and proved that the proposed calibration methods can be efficient.

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Rectification Using Different Types of Cameras Attached to a Vehicle

Cited by 15 publications

References 21 publications

Multi-Sensor Extrinsic Calibration Using an Extended Set of Pairwise Geometric Transformations

Multi-Sensor Extrinsic Calibration Using an Extended Set of Pairwise Geometric Transformations

Accurate Uncalibrated Rectification Using Feature Matching Re-Selection

Image Distortion and Rectification Calibration Algorithms and Validation Technique for a Stereo Camera

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