Monocular Camera Calibration for Autonomous Driving — a comparative study

Martins, Pedro Filipe; Costelha, Hugo; Bento, Luis Conde; Neves, Carlos

doi:10.1109/icarsc49921.2020.9096104

Cited by 8 publications

(6 citation statements)

References 16 publications

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“…A number of distortion models have been proposed. Given a enough number of correspondences, it is known that using camera models with larger number of parameters can achieve the accurate camera calibration [40]. However, through the experiments, we found the complex camera models are often not suitable for our conditions where the correspondences are only sparsely obtained.…”

Section: B Distortion Model Selectionmentioning

confidence: 95%

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Accuracy Evaluation and Prediction of Single-Image Camera Calibration

et al. 2023

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This paper proposes an application to statistically predict the accuracy of single-image geometric camera calibration that uses given 2D-3D correspondences. Deriving both camera intrinsics and extrinsics from correspondences between a single image and a 3D shape, is important for the scene analysis when the optical system of the camera is lost, such as in the analyses of traffic accidents. It is unclear whether the single-image calibration will be successful in practice, particularly when the number of 2D-3D correspondences is small, even if we could assign accurate correspondences by manual labor. To this end, we perform a systematic evaluation of the camera parameter accuracy using synthetic environments. Based on the statistics observed during the experiments, our application predicts the calibration accuracy from simple variables (e.g., the area that correspondences could be given). Since the prediction process does not rely on 3D shapes, it provides an estimate of the success of the calibration before time-consuming processes, i.e., 3D scanning and 2D-3D correspondence mapping.INDEX TERMS Camera calibration, traffic accident reconstruction, computer vision.

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Section: B Distortion Model Selectionmentioning

confidence: 95%

“…Several studies perform task-oriented evaluation, which assesses the influence of calibration error to the accuracy of stereo vision [35], [36] or 3D reconstruction [37]- [39]. Also, a recent paper [40] seeks the camera calibration options suitable for autonomous driving applications.…”

Section: Evaluation Of Calibration Accuracymentioning

confidence: 99%

Accuracy Evaluation and Prediction of Single-Image Camera Calibration

et al. 2023

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“…There are a vast array of internal and external noise factors which will affect the data produced by each perception sensor, and this degradation can have implication on perception algorithms and on the overall automated system. Some of these noise factors can be common between the sensors, such as weather, whilst others can be more specific to each sensor, such as lens effects for camera or radome effects for RADAR [31], [32].…”

Section: B Noise Factors and Automotive Perception Sensorsmentioning

confidence: 99%

A noise analysis of 4D RADAR: robust sensing for automotive?

Chan,

Shahbeigi Roudposhti,

et al. 2023

Preprint

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<p>The sensor suite for assisted and automated driving functions vehicle is critical to the function of a vehicle, but also the first and most important limitation to the level of automation that the system can achieve. The advancement of 4D RADARs, providing better resolution in both azimuth and elevation compared to traditional RADAR, can assist to achieve more robust situational awareness, whilst also providing more data for perception algorithms and sensor fusion. However, like all perception sensors, 4D RADAR is also affected by numerous noise factors. To explore the sources of noise, this work identifies, classifies, and analyses automotive 4D RADAR noise factors. Overall, 22 noise factors have been considered, in combination with their effect on six 4D RADAR outputs. Finally, this work also presents and applies, for the first time, a dissimilarity metric to collected 4D RADAR data in the presence of rain with different intensities. The proposed metric is used to assess the effect of noise on the variability of the measured data, in addition it can be used to compare any 4D RADAR data. The metric, combined with other pointcloud evaluations, shows that as rainrate intensifies, the size of the pointcloud decreases, but also the variation in the measurements increases. This work presents the importance of evaluating, companding, and quantifying noise for 4D RADAR, and can pave the way for more in depth analysis of its modelling and testing of 4D RADAR for assisted and automated driving functions.</p>

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“…The specified coordinate function COORDS(*) and time function TIME(1) are applied to define the loading area and realize the movement of the load [20]. Equation (1) indicates that the loading area moves at a constant speed along the Xaxis, and the positive direction of the X-axis is defined as the driving direction.…”

Section: B Random Non-uniform Moving Load Settingmentioning

confidence: 99%

Data Fusion Model of Road Sensors based IoT Feature Clustering

Ye¹

2022

IJACSA

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The collection of traffic data can play a role in analyzing and predicting highway design, planning, and realtime traffic management. The accuracy requirements for road dynamic data collection are low, and the accuracy is usually 3%-5%. However, it is required that vehicles can pass at high speed and obtain traffic information such as vehicle classification and vehicle speed. The prerequisite for the application of Internet of Things (IoT) technology to road information monitoring lies in the research and development of sensor technology in the perception layer and communication technology in the network layer, so that can obtain a large amount of perception data to serve the development and application of algorithms. To achieve the goal of low-cost and long-term monitoring of comprehensive traffic information and road service status information, this paper constructs a road vibration monitoring system, carries out road vibration monitoring under complex road environments, and proposes a traffic information monitoring method driven by road vibration data. By deploying the pavement vibration monitoring system in the actual road, the original signal of pavement vibration under the action of vehicle moving load is obtained. Through smooth processing and eigenvalue extraction, the monitoring of vehicle speed, wheelbase driving direction, vehicle load position and traffic flow is realized. The experimental results prove that the analysis of the road dynamic response under working conditions, as well as smoothing processing and eigenvalue extraction, the numerical modeling method in this paper realizes the monitoring of the position of the vehicle load and the traffic flow. The calculation error of vehicle speed and wheelbase is within ±4%, which is helpful to find the characteristic index of road vibration signal for evaluating road service status, and provides a reference for the application of road vibration response in road damage early warning and scientific maintenance.

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Monocular Camera Calibration for Autonomous Driving — a comparative study

Cited by 8 publications

References 16 publications

Accuracy Evaluation and Prediction of Single-Image Camera Calibration

Accuracy Evaluation and Prediction of Single-Image Camera Calibration

A noise analysis of 4D RADAR: robust sensing for automotive?

Data Fusion Model of Road Sensors based IoT Feature Clustering

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