Distorted pinhole camera modeling and calibration

Juarez-Salazar, Rigoberto; Zheng, Jiang Yu; Díaz-Ramírez, Víctor H.

doi:10.1364/ao.412159

Cited by 48 publications

(24 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The pinhole model is usually used when calibrating the camera's intrinsic, but the pinhole model is just a simplified model of the camera projection process [44]. The actual camera lens group is more complex and does not have an absolute optical center point [45]. Due to the complex internal structure of the camera, lacking an effective quantitative evaluation method for the camera's intrinsic calibration.…”

Section: A Manual Target-less Calibration Toolsmentioning

confidence: 99%

OpenCalib: A Multi-sensor Calibration Toolbox for Autonomous Driving

Yan¹,

Zhuochun²,

Wang³

et al. 2022

Preprint

View full text Add to dashboard Cite

Accurate sensor calibration is a prerequisite for multi-sensor perception and localization systems for autonomous vehicles. The intrinsic parameter calibration of the sensor is to obtain the mapping relationship inside the sensor, and the extrinsic parameter calibration is to transform two or more sensors into a unified spatial coordinate system. Most sensors need to be calibrated after installation to ensure the accuracy of sensor measurements. To this end, we present OpenCalib, a calibration toolbox that contains a rich set of various sensor calibration methods. OpenCalib covers manual calibration tools, automatic calibration tools, factory calibration tools, and online calibration tools for different application scenarios. At the same time, to evaluate the calibration accuracy and subsequently improve the accuracy of the calibration algorithm, we released a corresponding benchmark dataset. This paper introduces various features and calibration methods of this toolbox. To our knowledge, this is the first open-sourced calibration codebase containing the full set of autonomousdriving-related calibration approaches in this area. We wish that the toolbox could be helpful to autonomous driving researchers. We have open-sourced our code on GitHub to benefit the community.

show abstract

Section: A Manual Target-less Calibration Toolsmentioning

confidence: 99%

OpenCalib: A Multi-sensor Calibration Toolbox for Autonomous Driving

Yan¹,

Zhuochun²,

Wang³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…The extrinsic matrix is presented in Equation ( 1 ), where the LiDAR points in the camera frame are expressed according to the extrinsic parameters matrix (LiDAR to camera transition) and the LiDAR points. Firstly, we recover the LiDAR points file that coordinates are expressed in the LiDAR reference frame, then we multiply this matrix by the extrinsic parameters matrix composed of the rotation and translation parameters to place the LiDAR points in the camera reference frame (Equation ( 1 )) [ 54 , 55 ].

where

is rotation verctor R ,

is translation verctor T , and

are measured in mm.…”

Section: Real Multimodal Road and Railway Datasetmentioning

confidence: 99%

“…

where

is rotation verctor R ,

is translation verctor T , and

are measured in mm. Secondly, the matrix of LiDAR points expressed in the camera frame obtained in Equation ( 1 )) is used with the matrix of intrinsic parameters of the camera (camera-to-image pixel transition) to compute the pixel coordinates of the image, as it is presented in Equation ( 2 )) [ 54 , 55 ].

where

are measured in

are the pixel coordinates of the optical axis projection, and

are the intrinsic parameters of the camera.…”

Section: Real Multimodal Road and Railway Datasetmentioning

confidence: 99%

Road and Railway Smart Mobility: A High-Definition Ground Truth Hybrid Dataset

Khemmar

Mauri

Dulompont

et al. 2022

Sensors

View full text Add to dashboard Cite

A robust visual understanding of complex urban environments using passive optical sensors is an onerous and essential task for autonomous navigation. The problem is heavily characterized by the quality of the available dataset and the number of instances it includes. Regardless of the benchmark results of perception algorithms, a model would only be reliable and capable of enhanced decision making if the dataset covers the exact domain of the end-use case. For this purpose, in order to improve the level of instances in datasets used for the training and validation of Autonomous Vehicles (AV), Advanced Driver Assistance Systems (ADAS), and autonomous driving, and to reduce the void due to the no-existence of any datasets in the context of railway smart mobility, we introduce our multimodal hybrid dataset called ESRORAD. ESRORAD is comprised of 34 videos, 2.7 k virtual images, and 100 k real images for both road and railway scenes collected in two Normandy towns, Rouen and Le Havre. All the images are annotated with 3D bounding boxes showing at least three different classes of persons, cars, and bicycles. Crucially, our dataset is the first of its kind with uncompromised efforts on being the best in terms of large volume, abundance in annotation, and diversity in scenes. Our escorting study provides an in-depth analysis of the dataset’s characteristics as well as a performance evaluation with various state-of-the-art models trained under other popular datasets, namely, KITTI and NUScenes. Some examples of image annotations and the prediction results of our 3D object detection lightweight algorithms are available in ESRORAD dataset. Finally, the dataset is available online. This repository consists of 52 datasets with their respective annotations performed.

show abstract

“…Due to the complex internal structure of the camera and the way of data acquisition, the camera's intrinsic parameters are usually unstable in calibration [12]. On the other hand, the inaccuracy of the camera's intrinsic parameters is because the actual camera projection process and the pinhole model are not completely corresponding, and the equivalent camera optical center points at different distances are different [13]. Barrel distortion usually occurs at short focal lengths, and pincushion distortion usually occurs at long focal lengths [33].…”

Section: ) Camera Intrinsic Calibration Consistency Evaluationmentioning

confidence: 99%

“…The pinhole model is usually used when calibrating the camera's intrinsic, but the actual camera projection process and the pinhole model are not completely corresponding [12]. The actual camera lens group is more complex and does not have an absolute optical center point [13]. As shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

Joint Camera Intrinsic and LiDAR-Camera Extrinsic Calibration

Yan¹,

Feng²,

Shi³

et al. 2022

Preprint

View full text Add to dashboard Cite

Sensor-based environmental perception is a crucial step for autonomous driving systems, for which an accurate calibration between multiple sensors plays a critical role. For the calibration of LiDAR and camera, the existing method is generally to calibrate the intrinsic of the camera first and then calibrate the extrinsic of the LiDAR and camera. If the camera's intrinsic is not calibrated correctly in the first stage, it isn't easy to calibrate the LiDAR-camera extrinsic accurately. Due to the complex internal structure of the camera and the lack of an effective quantitative evaluation method for the camera's intrinsic calibration, in the actual calibration, the accuracy of extrinsic parameter calibration is often reduced due to the tiny error of the camera's intrinsic parameters. To this end, we propose a novel target-based joint calibration method of the camera intrinsic and LiDAR-camera extrinsic parameters. Firstly, we design a novel calibration board pattern, adding four circular holes around the checkerboard for locating the LiDAR pose. Subsequently, a cost function defined under the reprojection constraints of the checkerboard and circular holes features is designed to solve the camera's intrinsic parameters, distortion factor, and LiDAR-camera extrinsic parameter. In the end, quantitative and qualitative experiments are conducted in actual and simulated environments, and the result shows the proposed method can achieve accuracy and robustness performance. The open-source code is available at https://github.com/OpenCalib/JointCalib.

show abstract

Distorted pinhole camera modeling and calibration

Cited by 48 publications

References 32 publications

OpenCalib: A Multi-sensor Calibration Toolbox for Autonomous Driving

OpenCalib: A Multi-sensor Calibration Toolbox for Autonomous Driving

Road and Railway Smart Mobility: A High-Definition Ground Truth Hybrid Dataset

Joint Camera Intrinsic and LiDAR-Camera Extrinsic Calibration

Contact Info

Product

Resources

About