755 research outputs found
Trifocal Relative Pose from Lines at Points and its Efficient Solution
We present a new minimal problem for relative pose estimation mixing point
features with lines incident at points observed in three views and its
efficient homotopy continuation solver. We demonstrate the generality of the
approach by analyzing and solving an additional problem with mixed point and
line correspondences in three views. The minimal problems include
correspondences of (i) three points and one line and (ii) three points and two
lines through two of the points which is reported and analyzed here for the
first time. These are difficult to solve, as they have 216 and - as shown here
- 312 solutions, but cover important practical situations when line and point
features appear together, e.g., in urban scenes or when observing curves. We
demonstrate that even such difficult problems can be solved robustly using a
suitable homotopy continuation technique and we provide an implementation
optimized for minimal problems that can be integrated into engineering
applications. Our simulated and real experiments demonstrate our solvers in the
camera geometry computation task in structure from motion. We show that new
solvers allow for reconstructing challenging scenes where the standard two-view
initialization of structure from motion fails.Comment: This material is based upon work supported by the National Science
Foundation under Grant No. DMS-1439786 while most authors were in residence
at Brown University's Institute for Computational and Experimental Research
in Mathematics -- ICERM, in Providence, R
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
An Efficient Solution to the Homography-Based Relative Pose Problem With a Common Reference Direction
International audienceIn this paper, we propose a novel approach to two-view minimal-case relative pose problems based on homography with a common reference direction. We explore the rank-1 constraint on the difference between the Euclidean homog-raphy matrix and the corresponding rotation, and propose an efficient two-step solution for solving both the calibrated and partially calibrated (unknown focal length) problems. We derive new 3.5-point, 3.5-point, 4-point solvers for two cameras such that the two focal lengths are unknown but equal, one of them is unknown, and both are unknown and possibly different, respectively. We present detailed analyses and comparisons with existing 6-and 7-point solvers, including results with smart phone images
Affine Correspondences between Multi-Camera Systems for Relative Pose Estimation
We present a novel method to compute the relative pose of multi-camera
systems using two affine correspondences (ACs). Existing solutions to the
multi-camera relative pose estimation are either restricted to special cases of
motion, have too high computational complexity, or require too many point
correspondences (PCs). Thus, these solvers impede an efficient or accurate
relative pose estimation when applying RANSAC as a robust estimator. This paper
shows that the 6DOF relative pose estimation problem using ACs permits a
feasible minimal solution, when exploiting the geometric constraints between
ACs and multi-camera systems using a special parameterization. We present a
problem formulation based on two ACs that encompass two common types of ACs
across two views, i.e., inter-camera and intra-camera. Moreover, the framework
for generating the minimal solvers can be extended to solve various relative
pose estimation problems, e.g., 5DOF relative pose estimation with known
rotation angle prior. Experiments on both virtual and real multi-camera systems
prove that the proposed solvers are more efficient than the state-of-the-art
algorithms, while resulting in a better relative pose accuracy. Source code is
available at https://github.com/jizhaox/relpose-mcs-depth
3D Visual Perception for Self-Driving Cars using a Multi-Camera System: Calibration, Mapping, Localization, and Obstacle Detection
Cameras are a crucial exteroceptive sensor for self-driving cars as they are
low-cost and small, provide appearance information about the environment, and
work in various weather conditions. They can be used for multiple purposes such
as visual navigation and obstacle detection. We can use a surround multi-camera
system to cover the full 360-degree field-of-view around the car. In this way,
we avoid blind spots which can otherwise lead to accidents. To minimize the
number of cameras needed for surround perception, we utilize fisheye cameras.
Consequently, standard vision pipelines for 3D mapping, visual localization,
obstacle detection, etc. need to be adapted to take full advantage of the
availability of multiple cameras rather than treat each camera individually. In
addition, processing of fisheye images has to be supported. In this paper, we
describe the camera calibration and subsequent processing pipeline for
multi-fisheye-camera systems developed as part of the V-Charge project. This
project seeks to enable automated valet parking for self-driving cars. Our
pipeline is able to precisely calibrate multi-camera systems, build sparse 3D
maps for visual navigation, visually localize the car with respect to these
maps, generate accurate dense maps, as well as detect obstacles based on
real-time depth map extraction
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
P1AC: Revisiting Absolute Pose From a Single Affine Correspondence
We introduce a novel solution to the problem of estimating the pose of a
calibrated camera given a single observation of an oriented point and an affine
correspondence to a reference image. Affine correspondences have traditionally
been used to improve feature matching over wide baselines; however, little
previous work has considered the use of such correspondences for absolute
camera pose computation. The advantage of our approach (P1AC) is that it
requires only a single correspondence in the minimal case in comparison to the
traditional point-based approach (P3P) which requires at least three points.
Our method removes the limiting assumptions made in previous work and provides
a general solution that is applicable to large-scale image-based localization.
Our evaluation on synthetic data shows that our approach is numerically stable
and more robust to point observation noise than P3P. We also evaluate the
application of our approach for large-scale image-based localization and
demonstrate a practical reduction in the number of iterations and computation
time required to robustly localize an image
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