Calibration and correction of vignetting effects with an application to 3D mapping

Alexandrov, Sergey V.; Prankl, Johann; Zillich, Michael; Vincze, Markus

doi:10.1109/iros.2016.7759621

Cited by 13 publications

(6 citation statements)

References 14 publications

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“…The 3D mapping systems are sensitive to the unbalanced illumination caused by the vignetting of the optical system in Alexandrov, Prankl, Zillich, and Vincze (2016). Illumination balancing in the ROI images is necessary.…”

Section: Fisheye Cameramentioning

confidence: 99%

Autonomous aerial robot using dual‐fisheye cameras

Gao

Kaixuan

Ding

et al. 2020

Journal of Field Robotics

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Safety is undoubtedly the most fundamental requirement for any aerial robotic application. It is essential to equip aerial robots with omnidirectional perception coverage to ensure safe navigation in complex environments. In this paper, we present a light-weight and low-cost omnidirectional perception system, which consists of two ultrawide field-of-view (FOV) fisheye cameras and a low-cost inertial measurement unit (IMU). The goal of the system is to achieve spherical omnidirectional sensing coverage with the minimum sensor suite. The two fisheye cameras are mounted rigidly facing upward and downward directions and provide omnidirectional perception coverage: 360°FOV horizontally, 50°FOV vertically for stereo, and whole spherical for monocular. We present a novel optimization-based dualfisheye visual-inertial state estimator to provide highly accurate state-estimation.Real-time omnidirectional three-dimensional (3D) mapping is combined with stereobased depth perception for the horizontal direction and monocular depth perception for upward and downward directions. The omnidirectional perception system is integrated with online trajectory planners to achieve closed-loop, fully autonomous navigation. All computations are done onboard on a heterogeneous computing suite.Extensive experimental results are presented to validate individual modules as well as the overall system in both indoor and outdoor environments.

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Section: Fisheye Cameramentioning

confidence: 99%

Autonomous aerial robot using dual‐fisheye cameras

Gao

Kaixuan

Ding

et al. 2020

Journal of Field Robotics

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“…A set of LDR images Z i is taken at different exposure times t i . Each of them is converted into irradiance imageÊ i according to (1). The irradiance estimates in these images are in a linear space at a fixed common scale, which allows us to combine them using a weighted average:…”

Section: Hdr Imagingmentioning

confidence: 99%

“…As the absolute scale is not important, the calibrated response function is normalized such that g(255) = 1. The latter is calibrated using the method of Alexandrov et al [1]. It is worth noting that their method can not separate the vignetting effects from PRNU, jointly modeling them as single attenuation factor per pixel.…”

Section: Calibrationmentioning

confidence: 99%

“…The model ( 6) ignores spatially varying error sources. However, it was demonstrated that vignetting effects are significant in RGB-D cameras; correcting them has a positive impact on both tracking accuracy and map quality [1,5]. Therefore, we include vignetting effects and propose at the following model of individual pixel intensity:…”

Section: Camera Noise Modelmentioning

confidence: 99%

“…The quality of color in dense 3D mapping received less attention despite being equally important in applications such as virtual and augmented reality, semantic analysis, and recognition. Recent works demonstrated an improvement in the texture quality through radiometric calibration of the camera [1], subsequent offline texture map generation and optimization of camera parameters [13,28], online estimation of surface reflectance [14] and light sources within the reconstructed scenes [25].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

High Dynamic Range SLAM with Map-Aware Exposure Time Control

Alexandrov

Prankl

Zillich

et al. 2017

2017 International Conference on 3D Vision (3DV)

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The research in dense online 3D mapping is mostly focused on the geometrical accuracy and spatial extent of the reconstructions. Their color appearance is often neglected, leading to inconsistent colors and noticeable artifacts. We rectify this by extending a state-of-the-art SLAM system to accumulate colors in HDR space. We replace the simplistic pixel intensity averaging scheme with HDR color fusion rules tailored to the incremental nature of SLAM and a noise model suitable for off-the-shelf RGB-D cameras. Our main contribution is a map-aware exposure time controller. It makes decisions based on the global state of the map and predicted camera motion, attempting to maximize the information gain of each observation. We report a set of experiments demonstrating the improved texture quality and advantages of using the custom controller that is tightly integrated in the mapping loop.

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RGB-D Sensors Data Quality Assessment and Improvement for Advanced Applications

Rodríguez‐Gonzálvez

Guidi

2019

Advances in Computer Vision and Pattern Recognition

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Calibration and correction of vignetting effects with an application to 3D mapping

Cited by 13 publications

References 14 publications

Autonomous aerial robot using dual‐fisheye cameras

Autonomous aerial robot using dual‐fisheye cameras

High Dynamic Range SLAM with Map-Aware Exposure Time Control

RGB-D Sensors Data Quality Assessment and Improvement for Advanced Applications

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