69 research outputs found
Calibrated and Partially Calibrated Semi-Generalized Homographies
In this paper, we propose the first minimal solutions for estimating the
semi-generalized homography given a perspective and a generalized camera. The
proposed solvers use five 2D-2D image point correspondences induced by a scene
plane. One of them assumes the perspective camera to be fully calibrated, while
the other solver estimates the unknown focal length together with the absolute
pose parameters. This setup is particularly important in structure-from-motion
and image-based localization pipelines, where a new camera is localized in each
step with respect to a set of known cameras and 2D-3D correspondences might not
be available. As a consequence of a clever parametrization and the elimination
ideal method, our approach only needs to solve a univariate polynomial of
degree five or three. The proposed solvers are stable and efficient as
demonstrated by a number of synthetic and real-world experiments
Pose Estimation for Vehicle-mounted Cameras via Horizontal and Vertical Planes
We propose two novel solvers for estimating the egomotion of a calibrated
camera mounted to a moving vehicle from a single affine correspondence via
recovering special homographies. For the first class of solvers, the sought
plane is expected to be perpendicular to one of the camera axes. For the second
class, the plane is orthogonal to the ground with unknown normal, e.g., it is a
building facade. Both methods are solved via a linear system with a small
coefficient matrix, thus, being extremely efficient. Both the minimal and
over-determined cases can be solved by the proposed methods. They are tested on
synthetic data and on publicly available real-world datasets. The novel methods
are more accurate or comparable to the traditional algorithms and are faster
when included in state of the art robust estimators
Globally Optimal Relative Pose Estimation with Gravity Prior
Smartphones, tablets and camera systems used, e.g., in cars and UAVs, are
typically equipped with IMUs (inertial measurement units) that can measure the
gravity vector accurately. Using this additional information, the -axes of
the cameras can be aligned, reducing their relative orientation to a single
degree-of-freedom. With this assumption, we propose a novel globally optimal
solver, minimizing the algebraic error in the least-squares sense, to estimate
the relative pose in the over-determined case. Based on the epipolar
constraint, we convert the optimization problem into solving two polynomials
with only two unknowns. Also, a fast solver is proposed using the first-order
approximation of the rotation. The proposed solvers are compared with the
state-of-the-art ones on four real-world datasets with approx. 50000 image
pairs in total. Moreover, we collected a dataset, by a smartphone, consisting
of 10933 image pairs, gravity directions, and ground truth 3D reconstructions
Efficient Min-cost Flow Tracking with Bounded Memory and Computation
This thesis is a contribution to solving multi-target tracking in an optimal fashion for real-time demanding computer vision applications. We introduce a challenging benchmark, recorded with our autonomous driving platform AnnieWAY. Three main challenges of tracking are addressed: Solving the data association (min-cost flow) problem faster than standard solvers, extending this approach to an online setting, and making it real-time capable by a tight approximation of the optimal solution
Sparse Monocular Scene Reconstruction Using Rolling Voxel Maps
We present a method for creating 3D obstacle maps in real-time using only a monocular camera and an inertial measurement unit (IMU). We track a large amount of sparse features in the image frame. Then, given scale-accurate pose estimates from a front-end visual-inertial odometry (VIO) algorithm, we estimate the inverse depth to each of the tracked features using a keyframe-based feature-only bundle adjustment. These features are then accumulated within a probabilistic robocentric 3D voxel map that rolls as the camera moves. This local rolling voxel map provides a simple scene representation within which obstacle avoidance planning can easily be done. Our system is capable of running at camera frame rate on a laptop CPU
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