794 research outputs found
Robust Intrinsic and Extrinsic Calibration of RGB-D Cameras
Color-depth cameras (RGB-D cameras) have become the primary sensors in most
robotics systems, from service robotics to industrial robotics applications.
Typical consumer-grade RGB-D cameras are provided with a coarse intrinsic and
extrinsic calibration that generally does not meet the accuracy requirements
needed by many robotics applications (e.g., highly accurate 3D environment
reconstruction and mapping, high precision object recognition and localization,
...). In this paper, we propose a human-friendly, reliable and accurate
calibration framework that enables to easily estimate both the intrinsic and
extrinsic parameters of a general color-depth sensor couple. Our approach is
based on a novel two components error model. This model unifies the error
sources of RGB-D pairs based on different technologies, such as
structured-light 3D cameras and time-of-flight cameras. Our method provides
some important advantages compared to other state-of-the-art systems: it is
general (i.e., well suited for different types of sensors), based on an easy
and stable calibration protocol, provides a greater calibration accuracy, and
has been implemented within the ROS robotics framework. We report detailed
experimental validations and performance comparisons to support our statements
RCDN -- Robust X-Corner Detection Algorithm based on Advanced CNN Model
Accurate detection and localization of X-corner on both planar and non-planar
patterns is a core step in robotics and machine vision. However, previous works
could not make a good balance between accuracy and robustness, which are both
crucial criteria to evaluate the detectors performance. To address this
problem, in this paper we present a novel detection algorithm which can
maintain high sub-pixel precision on inputs under multiple interference, such
as lens distortion, extreme poses and noise. The whole algorithm, adopting a
coarse-to-fine strategy, contains a X-corner detection network and three
post-processing techniques to distinguish the correct corner candidates, as
well as a mixed sub-pixel refinement technique and an improved region growth
strategy to recover the checkerboard pattern partially visible or occluded
automatically. Evaluations on real and synthetic images indicate that the
presented algorithm has the higher detection rate, sub-pixel accuracy and
robustness than other commonly used methods. Finally, experiments of camera
calibration and pose estimation verify it can also get smaller re-projection
error in quantitative comparisons to the state-of-the-art.Comment: 15 pages, 8 figures and 4 tables. Unpublished further research and
experiments of Checkerboard corner detection network CCDN (arXiv:2302.05097)
and application exploration for robust camera calibration
(https://ieeexplore.ieee.org/abstract/document/9428389
Calibration and Sensitivity Analysis of a Stereo Vision-Based Driver Assistance System
Az http://intechweb.org/ alatti "Books" fĂĽl alatt kell rákeresni a "Stereo Vision" cĂmre Ă©s az 1. fejezetre
VINS-mono Optimized: A Monocular Visual-inertial State Estimator with Improved Initialization
State estimation is one of the key areas in robotics. It touches a variety of applications in practice such as, aerial vehicle navigation, autonomous driving, augmented reality, and virtual reality. A monocular visual-inertial system (VINS) is one of the popular trends in solving state estimation. By fusing a monocular camera and IMU properly, the system is capable of providing the position and orientation of a vehicle and recovering the scale.
One of the challenges for a monocular VINS is estimator initialization due to the inadequacy of direct distance measurement. Based on the work of Hong Kong University of Technology on monocular VINS, a checkerboard pattern is introduced to improve the original initialization process. The checkerboard parameters are used along with the calculated 3D coordinates to replace the original initialization process, leading to higher accuracy. The results demonstrated lowered cross track error and final drift, compared with the original approach
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