DLT-Like Calibration of Central Catadioptric Cameras

International audienceIn this study, we present a calibration technique that is valid for all single-viewpoint catadioptric cameras. We are able to represent the projection of 3D points on a catadioptric image linearly with a 6 × 10 projection matrix, which uses lifted coordinates for image and 3D points. This projection matrix can be computed with enough number of 3D-2D correspondences (minimum 20 points distributed in three different planes). We show how to decompose it to obtain intrinsic and extrinsic parameters. Moreover, we use this parameter estimation followed by a non-linear optimization to calibrate various types of cameras. Our results are based on the sphere camera model which considers that every central catadioptric system can be modeled using two projections, one from 3D points to a unitary sphere and then a perspective projection from the sphere to the image plane. We tested our method both with simulations and real images

Plane-Based Calibration of Central Catadioptric Cameras

International audienceWe present a novel calibration technique for all central catadioptric cameras using images of planar grids. We adopted the well-known sphere camera model to describe the catadioptric projection. We show that, using the so-called lifted coordinates, a linear relation mapping the grid points to the corresponding points on the image plane can be written as a 6 × 6 matrix Hcata , which acts like the classical 3 × 3 ho- mography for perspective cameras. We show how to compute the image of the absolute conic (IAC) from at least 3 homo- graphies and how to recover from it the intrinsic parameters of the catadioptric camera. In the case of paracatadioptric cameras one such homography is enough to estimate the IAC, thus allowing the calibration from a single image

Calibration and disparity maps for a depth camera based on a four-lens device

We propose a model of depth camera based on a four-lens device. This device is used for validating alternate approaches for calibrating multiview cameras and also for computing disparity or depth images. The calibration method arises from previous works, where principles of variable homography were extended for three-dimensional (3-D) measurement. Here, calibration is performed between two contiguous views obtained on the same image sensor. This approach leads us to propose a new approach for simplifying calibration by using the properties of the variable homography. Here, the second part addresses new principles for obtaining disparity images without any matching. A fast algorithm using a contour propagation algorithm is proposed without requiring structured or random pattern projection. These principles are proposed in a framework of quality control by vision, for inspection in natural illumination. By preserving scene photometry, some other standard controls, as for example calipers, shape recognition, or barcode reading, can be done conjointly with 3-D measurements. Approaches presented here are evaluated. First, we show that rapid calibration is relevant for devices mounted with multiple lenses. Second, synthetic and real experimentations validate our method for computing depth images

Enhancing 3D Visual Odometry with Single-Camera Stereo Omnidirectional Systems

We explore low-cost solutions for efficiently improving the 3D pose estimation problem of a single camera moving in an unfamiliar environment. The visual odometry (VO) task -- as it is called when using computer vision to estimate egomotion -- is of particular interest to mobile robots as well as humans with visual impairments. The payload capacity of small robots like micro-aerial vehicles (drones) requires the use of portable perception equipment, which is constrained by size, weight, energy consumption, and processing power. Using a single camera as the passive sensor for the VO task satisfies these requirements, and it motivates the proposed solutions presented in this thesis. To deliver the portability goal with a single off-the-shelf camera, we have taken two approaches: The first one, and the most extensively studied here, revolves around an unorthodox camera-mirrors configuration (catadioptrics) achieving a stereo omnidirectional system (SOS). The second approach relies on expanding the visual features from the scene into higher dimensionalities to track the pose of a conventional camera in a photogrammetric fashion. The first goal has many interdependent challenges, which we address as part of this thesis: SOS design, projection model, adequate calibration procedure, and application to VO. We show several practical advantages for the single-camera SOS due to its complete 360-degree stereo views, that other conventional 3D sensors lack due to their limited field of view. Since our omnidirectional stereo (omnistereo) views are captured by a single camera, a truly instantaneous pair of panoramic images is possible for 3D perception tasks. Finally, we address the VO problem as a direct multichannel tracking approach, which increases the pose estimation accuracy of the baseline method (i.e., using only grayscale or color information) under the photometric error minimization as the heart of the “direct” tracking algorithm. Currently, this solution has been tested on standard monocular cameras, but it could also be applied to an SOS. We believe the challenges that we attempted to solve have not been considered previously with the level of detail needed for successfully performing VO with a single camera as the ultimate goal in both real-life and simulated scenes

Reconstruction active et passive en vision par ordinateur

Thèse numérisée par la Direction des bibliothèques de l'Université de Montréal