An automatic online camera calibration system for vehicular applications

Zhao, Kaixuan; Iurgel, U.; Meuter, Mirko; Pauli, Josef

doi:10.1109/itsc.2014.6957643

Cited by 13 publications

(10 citation statements)

References 9 publications

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“…This method automatically detects lane markings, but manually rejects outliers for reliable parameter estimation. Zhao et al [ 25 ] estimated the pitch and yaw of the vehicle mounted camera. This method uses a vanishing point calculated by applying the weighted least squares approach to detected lane markings.…”

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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“…(2) Existing solutions which are feasible to the surround-view case mostly require relatively ideal environments. For example, the approaches proposed in [2,4,13,21,28] require that, on the ground, there must be two parallel lane-lines that can be clearly detected. Thus, they usually have noticeable limitations in both the usability and the generalization capability.…”

Section: Introductionmentioning

confidence: 99%

ROECS: A Robust Semi-direct Pipeline Towards Online Extrinsics Correction of the Surround-view System

Zhang

Zhao

Shen

et al. 2021

Proceedings of the 29th ACM International Conference on Multimedia

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Generally, a surround-view system (SVS), which is an indispensable component of advanced driving assistant systems (ADAS), consists of four to six wide-angle fisheye cameras. As long as both intrinsics and extrinsics of all cameras have been calibrated, a top-down surround-view with the real scale can be synthesized at runtime from fisheye images captured by these cameras. However, when the vehicle is driving on the road, relative poses between cameras in the SVS may change from the initial calibrated states due to bumps or collisions. In case that extrinsics' representations are not adjusted accordingly, on the surround-view, obvious geometric misalignment will appear. Currently, the researches on correcting the extrinsics of the SVS in an online manner are quite sporadic, and a mature and robust pipeline is still lacking. As an attempt to fill this research gap to some extent, in this work, we present a novel extrinsics correction pipeline designed specially for the SVS, namely ROECS (Robust Online Extrinsics Correction of the Surround-view system). Specifically, a "refined bi-camera error" model is firstly designed. Then, by minimizing the overall "bi-camera error" within a sparse and semi-direct framework, the SVS's extrinsics can be iteratively optimized and become accurate eventually. Besides, an innovative three-step pixel selection strategy is also proposed. The superior robustness and the generalization capability of ROECS are validated by both quantitative and qualitative experimental results. To make the results reproducible, the collected data and the source code have been released at https://cslinzhang.github.io/ROECS/.

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“…Therefore one has to lower the data-taking expectations finding a compromise between the two factors, which most of the time ends up reducing both the field of view and the resolution of the system. A smarter, though more complicated, strategy is to replace the static set-up with a dynamic one, effectively widening the field of view with a controlled rotation of the cameras aimed at following the targets, [24], [25], [26], [27], [28], [29], [30]. The dynamic set-up overcomes the limitation of the static one by actually breaking the link between the size of the field of view and the resolution of the system, which is now the only factor depending on the focal length.…”

Section: Introductionmentioning

confidence: 99%

CoMo: A Novel Comoving 3D Camera System

Cavagna

Xiao

Melillo

et al. 2021

IEEE Trans. Instrum. Meas.

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Motivated by the theoretical interest in reconstructing long 3D trajectories of individual birds in large flocks, we developed CoMo, a co-moving camera system of two synchronized cameras coupled with rotational stages, which allow us to dynamically follow the motion of a target flock. With the rotation of the cameras we overcome the limitations of standard static systems that restrict the duration of the collected data to the short interval of time in which targets are in the cameras common field of view, but at the same time we change in time the external parameters of the system, which have then to be calibrated frame-by-frame. We address the calibration of the external parameters measuring the position of the cameras and their three angles of yaw, pitch and roll in the system home configuration (rotational stage at an angle equal to 0 • ) and combining this static information with the time dependent rotation due to the stages. We evaluate the robustness and accuracy of the system by comparing reconstructed and measured 3D distances in what we call 3D tests, which show a relative error of the order of 1%. The novelty of the work presented in this paper is not only on the system itself, but also on the approach we use in the tests, which we show to be a very powerful tool in detecting and fixing calibration inaccuracies and that, for this reason, may be relevant for a broad audience.

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An automatic online camera calibration system for vehicular applications

Cited by 13 publications

References 9 publications

Automatic Calibration of an Around View Monitor System Exploiting Lane Markings

Automatic Calibration of an Around View Monitor System Exploiting Lane Markings

ROECS: A Robust Semi-direct Pipeline Towards Online Extrinsics Correction of the Surround-view System

CoMo: A Novel Comoving 3D Camera System

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