120 research outputs found
A clever elimination strategy for efficient minimal solvers
We present a new insight into the systematic generation of minimal solvers in
computer vision, which leads to smaller and faster solvers. Many minimal
problem formulations are coupled sets of linear and polynomial equations where
image measurements enter the linear equations only. We show that it is useful
to solve such systems by first eliminating all the unknowns that do not appear
in the linear equations and then extending solutions to the rest of unknowns.
This can be generalized to fully non-linear systems by linearization via
lifting. We demonstrate that this approach leads to more efficient solvers in
three problems of partially calibrated relative camera pose computation with
unknown focal length and/or radial distortion. Our approach also generates new
interesting constraints on the fundamental matrices of partially calibrated
cameras, which were not known before.Comment: 13 pages, 7 figure
Efficient solutions to the relative pose of three calibrated cameras from four points using virtual correspondences
We study the challenging problem of estimating the relative pose of three
calibrated cameras. We propose two novel solutions to the notoriously difficult
configuration of four points in three views, known as the 4p3v problem. Our
solutions are based on the simple idea of generating one additional virtual
point correspondence in two views by using the information from the locations
of the four input correspondences in the three views. For the first solver, we
train a network to predict this point correspondence. The second solver uses a
much simpler and more efficient strategy based on the mean points of three
corresponding input points. The new solvers are efficient and easy to implement
since they are based on the existing efficient minimal solvers, i.e., the
well-known 5-point relative pose and the P3P solvers. The solvers achieve
state-of-the-art results on real data. The idea of solving minimal problems
using virtual correspondences is general and can be applied to other problems,
e.g., the 5-point relative pose problem. In this way, minimal problems can be
solved using simpler non-minimal solvers or even using sub-minimal samples
inside RANSAC.
In addition, we compare different variants of 4p3v solvers with the baseline
solver for the minimal configuration consisting of three triplets of points and
two points visible in two views. We discuss which configuration of points is
potentially the most practical in real applications
RRD-SLAM: Radial-distorted rolling-shutter direct SLAM
In this paper, we present a monocular direct semi-dense SLAM (Simultaneous Localization And Mapping) method that can handle both radial distortion and rolling-shutter distortion. Such distortions are common in, but not restricted to, situations when an inexpensive wide-angle lens and a CMOS sensor are used, and leads to significant inaccuracy in the map and trajectory estimates if not modeled correctly. The apparent naive solution of simply undistorting the images using pre-calibrated parameters does not apply to this case since rows in the undistorted image are no longer captured at the same time. To address this we develop an algorithm that incorporates radial distortion into an existing state-of-the-art direct semi-dense SLAM system that takes rolling-shutters into account. We propose a method for finding the generalized epipolar curve for each rolling-shutter radially distorted image. Our experiments demonstrate the efficacy of our approach and compare it favorably with the state-of-the-art in direct semi-dense rolling-shutter SLAM.Jae-Hak Kim, Yasir Latif and Ian Rei
Trust Your IMU: Consequences of Ignoring the IMU Drift
In this paper, we argue that modern pre-integration methods for inertial
measurement units (IMUs) are accurate enough to ignore the drift for short time
intervals. This allows us to consider a simplified camera model, which in turn
admits further intrinsic calibration. We develop the first-ever solver to
jointly solve the relative pose problem with unknown and equal focal length and
radial distortion profile while utilizing the IMU data. Furthermore, we show
significant speed-up compared to state-of-the-art algorithms, with small or
negligible loss in accuracy for partially calibrated setups. The proposed
algorithms are tested on both synthetic and real data, where the latter is
focused on navigation using unmanned aerial vehicles (UAVs). We evaluate the
proposed solvers on different commercially available low-cost UAVs, and
demonstrate that the novel assumption on IMU drift is feasible in real-life
applications. The extended intrinsic auto-calibration enables us to use
distorted input images, making tedious calibration processes obsolete, compared
to current state-of-the-art methods
Making Affine Correspondences Work in Camera Geometry Computation
Local features e.g. SIFT and its affine and learned variants provide region-to-region rather than point-to-point correspondences. This has recently been exploited to create new minimal solvers for classical problems such as homography, essential and fundamental matrix estimation. The main advantage of such solvers is that their sample size is smaller, e.g., only two instead of four matches are required to estimate a homography. Works proposing such solvers often claim a significant improvement in run-time thanks to fewer RANSAC iterations. We show that this argument is not valid in practice if the solvers are used naively. To overcome this, we propose guidelines for effective use of region-to-region matches in the course of a full model estimation pipeline. We propose a method for refining the local feature geometries by symmetric intensity-based matching, combine uncertainty propagation inside RANSAC with preemptive model verification, show a general scheme for computing uncertainty of minimal solvers results, and adapt the sample cheirality check for homography estimation. Our experiments show that affine solvers can achieve accuracy comparable to point-based solvers at faster run-times when following our guidelines. We make code available at https://github.com/danini/affine-correspondences-for-camera-geometry
Odometria visual monocular em robĂ´s para a agricultura com camara(s) com lentes "olho de peixe"
One of the main challenges in robotics is to develop accurate localization methods that achieve acceptable runtime performances.One of the most common approaches is to use Global Navigation Satellite System such as GPS to localize robots.However, satellite signals are not full-time available in some kind of environments.The purpose of this dissertation is to develop a localization system for a ground robot.This robot is inserted in a project called RoMoVi and is intended to perform tasks like crop monitoring and harvesting in steep slope vineyards.This vineyards are localized in the Douro region which are characterized by the presence of high hills.Thus, the context of RoMoVi is not prosperous for the use of GPS-based localization systems.Therefore, the main goal of this work is to create a reliable localization system based on vision techniques and low cost sensors.To do so, a Visual Odometry system will be used.The concept of Visual Odometry is equivalent to wheel odometry but it has the advantage of not suffering from wheel slip which is present in these kind of environments due to the harsh terrain conditions.Here, motion is tracked computing the homogeneous transformation between camera frames, incrementally.However, this approach also presents some open issues.Most of the state of art methods, specially those who present a monocular camera system, don't perform good motion estimations in pure rotations.In some of them, motion even degenerates in these situations.Also, computing the motion scale is a difficult task that is widely investigated in this field.This work is intended to solve these issues.To do so, fisheye lens cameras will be used in order to achieve wide vision field of views
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