Hybrid Online Mobile Laser Scanner Calibration Through Image  Alignment by Mutual Information

Miled, Mourad; Soheilian, Bahman; Habets, Emmanuel; Vallet, Bruno

doi:10.5194/isprs-annals-iii-1-25-2016

Cited by 6 publications

(1 citation statement)

References 11 publications

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“…Statistical analysis-based registration methods are widespread for aligning an image to another image [ 15 ]; mutual information (MI) proposed by Viola [ 16 ] is the most commonly used statistical method. MI measures the similarity between two images based on the dependency of the intensity distribution [ 17 , 18 , 19 , 20 ]. Taylor and Nieto [ 21 ] proposed a modified form of MI using particle swarm optimization; Pascoe et al [ 22 ] introduced a normalized information distance metric based on MI and entropy variation to retrieve the camera position.…”

Section: Introductionmentioning

confidence: 99%

Registration of Panoramic/Fish-Eye Image Sequence and LiDAR Points Using Skyline Features

Zhu

Jia

2018

Sensors

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We propose utilizing a rigorous registration model and a skyline-based method for automatic registration of LiDAR points and a sequence of panoramic/fish-eye images in a mobile mapping system (MMS). This method can automatically optimize original registration parameters and avoid the use of manual interventions in control point-based registration methods. First, the rigorous registration model between the LiDAR points and the panoramic/fish-eye image was built. Second, skyline pixels from panoramic/fish-eye images and skyline points from the MMS’s LiDAR points were extracted, relying on the difference in the pixel values and the registration model, respectively. Third, a brute force optimization method was used to search for optimal matching parameters between skyline pixels and skyline points. In the experiments, the original registration method and the control point registration method were used to compare the accuracy of our method with a sequence of panoramic/fish-eye images. The result showed: (1) the panoramic/fish-eye image registration model is effective and can achieve high-precision registration of the image and the MMS’s LiDAR points; (2) the skyline-based registration method can automatically optimize the initial attitude parameters, realizing a high-precision registration of a panoramic/fish-eye image and the MMS’s LiDAR points; and (3) the attitude correction values of the sequences of panoramic/fish-eye images are different, and the values must be solved one by one.

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Section: Introductionmentioning

confidence: 99%

Registration of Panoramic/Fish-Eye Image Sequence and LiDAR Points Using Skyline Features

Zhu

Jia

2018

Sensors

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NRLI-UAV: Non-rigid registration of sequential raw laser scans and images for low-cost UAV LiDAR point cloud quality improvement

Wang

Chen

et al. 2019

ISPRS Journal of Photogrammetry and Remote Sensing

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Physics and semantic informed multi-sensor calibration via optimization theory and self-supervised learning

Hayoun,

Halachmi,

Serebro

et al. 2024

Sci Rep

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Widespread adaptation of autonomous, robotic systems relies greatly on safe and reliable operation, which in many cases is derived from the ability to maintain accurate and robust perception capabilities. Environmental and operational conditions as well as improper maintenance can produce calibration errors inhibiting sensor fusion and, consequently, degrading the perception performance and overall system usability. Traditionally, sensor calibration is performed in a controlled environment with one or more known targets. Such a procedure can only be carried out in between operations and is done manually; a tedious task if it must be conducted on a regular basis. This creates an acute need for online targetless methods, capable of yielding a set of geometric transformations based on perceived environmental features. However, the often-required redundancy in sensing modalities poses further challenges, as the features captured by each sensor and their distinctiveness may vary. We present a holistic approach to performing joint calibration of a camera–lidar–radar trio in a representative autonomous driving application. Leveraging prior knowledge and physical properties of these sensing modalities together with semantic information, we propose two targetless calibration methods within a cost minimization framework: the first via direct online optimization, and the second through self-supervised learning (SSL).

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Hybrid Online Mobile Laser Scanner Calibration Through Image Alignment by Mutual Information

Cited by 6 publications

References 11 publications

Registration of Panoramic/Fish-Eye Image Sequence and LiDAR Points Using Skyline Features

Registration of Panoramic/Fish-Eye Image Sequence and LiDAR Points Using Skyline Features

NRLI-UAV: Non-rigid registration of sequential raw laser scans and images for low-cost UAV LiDAR point cloud quality improvement

Physics and semantic informed multi-sensor calibration via optimization theory and self-supervised learning

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