Pose Self-Calibration of Stereo Vision Systems for Autonomous Vehicle Applications

Musleh, Basam; Martín, David; Armingol, José María; Escalera, Arturo de la

doi:10.3390/s16091492

Cited by 5 publications

(3 citation statements)

References 39 publications

(63 reference statements)

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“…The research by de Paula et al [15] also exploits known features on the road to achieve on-the-fly calibration of a stereo system. Methods directly estimating stereo parameters from image contents via visual landmarks were also researched [16,17].…”

Section: Related Workmentioning

confidence: 99%

Correcting Decalibration of Stereo Cameras in Self-Driving Vehicles

Muhovič

Perš

2020

Sensors

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Camera systems in autonomous vehicles are subject to various sources of anticipated and unanticipated mechanical stress (vibration, rough handling, collisions) in real-world conditions. Even moderate changes in camera geometry due to mechanical stress decalibrate multi-camera systems and corrupt downstream applications like depth perception. We propose an on-the-fly stereo recalibration method applicable in real-world autonomous vehicles. The method is comprised of two parts. First, in optimization step, external camera parameters are optimized with the goal to maximise the amount of recovered depth pixels. In the second step, external sensor is used to adjust the scaling of the optimized camera model. The method is lightweight and fast enough to run in parallel with stereo estimation, thus allowing an on-the-fly recalibration. Our extensive experimental analysis shows that our method achieves stereo reconstruction better or on par with manual calibration. If our method is used on a sequence of images, the quality of calibration can be improved even further.

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Section: Related Workmentioning

confidence: 99%

Correcting Decalibration of Stereo Cameras in Self-Driving Vehicles

Muhovič

Perš

2020

Sensors

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“…Besides these three approaches, there is a road geometry-based approach that calibrates vehicle-mounted cameras by estimating the geometry of the road in front of the vehicle [ 26 , 27 ]. Since almost all methods in this approach use stereo cameras to obtain three-dimensional geometry information of the road, it cannot be utilized for calibrating the cameras of the AVM system where there is very little overlap between views of adjacent cameras.…”

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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“…An efficient alternative to automatically calculate the distortion factor can be implemented through digital image processing algorithms [ 6 , 7 ], particularly using mathematical morphology, since this method is fundamentally based on the analysis of the shape. In order to do that, a shape measure is needed, for example, circularity.…”

Section: Introductionmentioning

confidence: 99%

Distortion Calculation Method Based on Image Processing for Automobile Lateral Mirrors

et al. 2022

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The automobile lateral-view mirrors are the most important visual support for driver safety; therefore, it is important they have robust quality control. Typically, the distortion of a lateral-view mirror is measured using the JIS-D-5705 standard; however, this methodology requires an expert person to perform the measurements and calculations manually, which can induce measurement errors. In this work, a semi-automatic distortion calculation method based on image processing is presented. Distortion calculations of five commercial mirrors from different manufacturers were performed, and a comparative study was carried out between the JIS-D-5705 standard and the proposed method. Experimental results performed according to the JIS-D-5705 standard showed that all mirrors have a distortion lower than 5%, indicating that all meet the standard. On the other hand, the proposed method was able to detect that one of the mirrors presented an important distortion, which was not detected by the methodology proposed in the standard; therefore, that mirror should not meet the standard. Then, it was possible to conclude that the proposed distortion calculation method, based on image processing, has higher robustness and precision than the standard. In addition, an appropriate and effective behavior against changes in scale, resolution, and, unlike the standard, against changes in image rotation was also shown.

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Pose Self-Calibration of Stereo Vision Systems for Autonomous Vehicle Applications

Cited by 5 publications

References 39 publications

Correcting Decalibration of Stereo Cameras in Self-Driving Vehicles

Correcting Decalibration of Stereo Cameras in Self-Driving Vehicles

Automatic Calibration of an Around View Monitor System Exploiting Lane Markings

Distortion Calculation Method Based on Image Processing for Automobile Lateral Mirrors

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