A Toolbox for Easily Calibrating Omnidirectional Cameras

By Davide Scaramuzza, Agostino Martinelli, Roland Yves Siegwart. 2006

783 citations recorded by the source corpus.

In this paper, we present a novel technique for calibrating central omnidirectional cameras. The proposed procedure is very fast and completely automatic, as the user is only asked to collect a few images of a checker board, and click on its corner points. In contrast with previous approaches, this technique does not use any specific model of the omnidirectional sensor. It only assumes that the imaging function can be described by a Taylor series expansion whose coefficients are estimated by solving a four-step least-squares linear minimization problem, followed by a non-linear refinement based on the maximum likelihood criterion. To validate the proposed technique, and evaluate its performance, we apply the calibration on both simulated and real data. Moreover, we show the calibration accuracy by projecting the color information of a calibrated camera on real 3D points extracted by a 3D sick laser range finder. Finally, we provide a Toolbox which implements the proposed calibration procedure

Subfields: Computer Vision and Pattern Recognition, Media Technology

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Citations in the corpus

A Survey of Autonomous Driving: Common Practices and Emerging Technologies (2019, neutral): “These are however difficult to intrinsically calibrate; the spherical images are highly distorted and the camera model used must account for mirror reflections or fisheye lens distortions, depending on the camera model producing the panoramic images [139], [140].”
Computer Vision for Autonomous Vehicles: Problems, Datasets and State-of-the-Art (2017, background): “Scaramuzza & Martinelli (2006) propose modeling the imaging function by a Taylor series expansion whose degree and the coefficients are the parameters to be estimated.”
A Deep Learning-based Radar and Camera Sensor Fusion Architecture for Object Detection (2019, methodology): “We use the calibration method presented by [28] to map the world coordinates to the image coordinates for our own data.”
Automatic camera and range sensor calibration using a single shot (2012, methodology): “Following [16], we use planar checkerboard patterns as calibration targets for multiple reasons: They are cheap to employ, corners can be localized with high sub-pixel accuracy and structure recovery profits from strong edges between corners.”
First-person hyper-lapse videos (2014, methodology): “We use the OCamCalib toolbox [Scaramuzza et al. 2006] to calibrate the lens distortion and convert the videos to (cropped) linear perspective projection.”
Omnidirectional 3D reconstruction in augmented Manhattan worlds (2014, methodology): “To ease the formulation of the 3D reconstruction problem we first compute a virtual 360◦ disparity image from four omnidirectional views captured by a catadioptric stereo pair at two consecutive time steps.”
An Enhanced Unified Camera Model (2016, methodology): “Equivalence between the unified model and the captured ray-based model as well as the pinhole model was shown in [4].”
1 year, 1000 km: The Oxford RobotCar dataset (2017, methodology): “For the Grasshopper2 images, one undistortion look-uptable is provided for each camera, generated using a calibrated model obtained using the OCamCalib toolbox of Scaramuzza et al. (2006). However, perspective undistortion of fisheye images often results in highly non-uniform images, and different m”
Single View Point Omnidirectional Camera Calibration from Planar Grids (2007, methodology): “In [10], the authors propose a method relying on a polynomial approximation of the projection function.”
Vision and IMU Data Fusion: Closed-Form Solutions for Attitude, Speed, Absolute Scale, and Bias Determination (2012, methodology): “The calibration of the camera was obtained by using the omnidirectional camera toolkit by Scaramuzza [26].”