50 research outputs found
OSPC: Online Sequential Photometric Calibration
Photometric calibration is essential to many computer vision applications.
One of its key benefits is enhancing the performance of Visual SLAM, especially
when it depends on a direct method for tracking, such as the standard KLT
algorithm. Another advantage could be in retrieving the sensor irradiance
values from measured intensities, as a pre-processing step for some vision
algorithms, such as shape-from-shading. Current photometric calibration systems
rely on a joint optimization problem and encounter an ambiguity in the
estimates, which can only be resolved using ground truth information. We
propose a novel method that solves for photometric parameters using a
sequential estimation approach. Our proposed method achieves high accuracy in
estimating all parameters; furthermore, the formulations are linear and convex,
which makes the solution fast and suitable for online applications. Experiments
on a Visual Odometry system validate the proposed method and demonstrate its
advantages
A Comprehensive Introduction of Visual-Inertial Navigation
In this article, a tutorial introduction to visual-inertial navigation(VIN)
is presented. Visual and inertial perception are two complementary sensing
modalities. Cameras and inertial measurement units (IMU) are the corresponding
sensors for these two modalities. The low cost and light weight of camera-IMU
sensor combinations make them ubiquitous in robotic navigation. Visual-inertial
Navigation is a state estimation problem, that estimates the ego-motion and
local environment of the sensor platform. This paper presents visual-inertial
navigation in the classical state estimation framework, first illustrating the
estimation problem in terms of state variables and system models, including
related quantities representations (Parameterizations), IMU dynamic and camera
measurement models, and corresponding general probabilistic graphical models
(Factor Graph). Secondly, we investigate the existing model-based estimation
methodologies, these involve filter-based and optimization-based frameworks and
related on-manifold operations. We also discuss the calibration of some
relevant parameters, also initialization of state of interest in
optimization-based frameworks. Then the evaluation and improvement of VIN in
terms of accuracy, efficiency, and robustness are discussed. Finally, we
briefly mention the recent development of learning-based methods that may
become alternatives to traditional model-based methods.Comment: 35 pages, 10 figure