2,767 research outputs found
On the Calibration of Active Binocular and RGBD Vision Systems for Dual-Arm Robots
This paper describes a camera and hand-eye
calibration methodology for integrating an active binocular
robot head within a dual-arm robot. For this purpose, we
derive the forward kinematic model of our active robot head
and describe our methodology for calibrating and integrating
our robot head. This rigid calibration provides a closedform
hand-to-eye solution. We then present an approach for
updating dynamically camera external parameters for optimal
3D reconstruction that are the foundation for robotic tasks such
as grasping and manipulating rigid and deformable objects. We
show from experimental results that our robot head achieves
an overall sub millimetre accuracy of less than 0.3 millimetres
while recovering the 3D structure of a scene. In addition, we
report a comparative study between current RGBD cameras
and our active stereo head within two dual-arm robotic testbeds
that demonstrates the accuracy and portability of our proposed
methodology
Dive Deeper into Rectifying Homography for Stereo Camera Online Self-Calibration
Accurate estimation of stereo camera extrinsic parameters is the key to
guarantee the performance of stereo matching algorithms. In prior arts, the
online self-calibration of stereo cameras has commonly been formulated as a
specialized visual odometry problem, without taking into account the principles
of stereo rectification. In this paper, we first delve deeply into the concept
of rectifying homography, which serves as the cornerstone for the development
of our novel stereo camera online self-calibration algorithm, for cases where
only a single pair of images is available. Furthermore, we introduce a simple
yet effective solution for global optimum extrinsic parameter estimation in the
presence of stereo video sequences. Additionally, we emphasize the
impracticality of using three Euler angles and three components in the
translation vectors for performance quantification. Instead, we introduce four
new evaluation metrics to quantify the robustness and accuracy of extrinsic
parameter estimation, applicable to both single-pair and multi-pair cases.
Extensive experiments conducted across indoor and outdoor environments using
various experimental setups validate the effectiveness of our proposed
algorithm. The comprehensive evaluation results demonstrate its superior
performance in comparison to the baseline algorithm. Our source code, demo
video, and supplement are publicly available at mias.group/StereoCalibrator
Flexible Stereo: Constrained, Non-rigid, Wide-baseline Stereo Vision for Fixed-wing Aerial Platforms
This paper proposes a computationally efficient method to estimate the
time-varying relative pose between two visual-inertial sensor rigs mounted on
the flexible wings of a fixed-wing unmanned aerial vehicle (UAV). The estimated
relative poses are used to generate highly accurate depth maps in real-time and
can be employed for obstacle avoidance in low-altitude flights or landing
maneuvers. The approach is structured as follows: Initially, a wing model is
identified by fitting a probability density function to measured deviations
from the nominal relative baseline transformation. At run-time, the prior
knowledge about the wing model is fused in an Extended Kalman filter~(EKF)
together with relative pose measurements obtained from solving a relative
perspective N-point problem (PNP), and the linear accelerations and angular
velocities measured by the two inertial measurement units (IMU) which are
rigidly attached to the cameras. Results obtained from extensive synthetic
experiments demonstrate that our proposed framework is able to estimate highly
accurate baseline transformations and depth maps.Comment: Accepted for publication in IEEE International Conference on Robotics
and Automation (ICRA), 2018, Brisban
Self-Calibration of Multi-Camera Systems for Vehicle Surround Sensing
Multi-camera systems are being deployed in a variety of vehicles and mobile robots today. To eliminate the need for cost and labor intensive maintenance and calibration, continuous self-calibration is highly desirable. In this book we present such an approach for self-calibration of multi-Camera systems for vehicle surround sensing. In an extensive evaluation we assess our algorithm quantitatively using real-world data
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