28,944 research outputs found
Eye-to-Eye Calibration: Extrinsische Kalibrierung von Mehrkamerasystemen mittels Hand-Auge-Kalibrierverfahren
The problem addressed in this thesis is the extrinsic calibration of embedded multi-camera systems without overlapping views, i.e., to determine the positions and orientations of rigidly coupled cameras with respect to a common coordinate frame from captured images. Such camera systems are of increasing interest for computer vision applications due to their large combined field of view, providing practical use for visual navigation and 3d scene reconstruction. However, in order to propagate observations from one camera to another, the parameters of the coordinate transformation between both cameras have to be determined accurately. Classical methods for extrinsic camera calibration relying on spatial correspondences between images cannot be applied here. The central topic of this work is an analysis of methods based on hand-eye calibration that exploit constraints of rigidly coupled motions to solve this problem from visual camera ego-motion estimation only, without need for additional sensors for pose tracking such as inertial measurement units or vehicle odometry. The resulting extrinsic calibration methods are referred to as "eye-to-eye calibration". We provide solutions based on pose measurements (geometric eye-to-eye calibration), decoupling the actual pose estimation from the extrinsic calibration, and solutions based on images measurements (visual eye-to-eye calibration), integrating both steps within a general Structure from Motion framework. Specific solutions are also proposed for critical motion configurations such as planar motion which often occurs in vehicle-based applications.Diese Arbeit beschĂ€ftigt sich mit der extrinsischen Kalibrierung von Mehrkamerasystemen ohne ĂŒberlappende Sichtbereiche aus Bildfolgen. Die extrinsischen Parameter fassen dabei Lage und Orientierung der als starr-gekoppelt vorausgesetzten Kameras in Bezug auf ein gemeinsames Referenzkoordinatensystem zusammen. Die Minimierung der Redundanz der einzelnen Sichtfelder zielt dabei auf ein möglichst groĂes kombiniertes Sichtfeld aller Kameras ab. Solche Aufnahmesysteme haben sich in den letzten Jahren als hilfreich fĂŒr eine Reihe von Aufgabenstellungen der Computer Vision erwiesen, z. B. in den Bereichen der visuellen Navigation und der bildbasierten 3D-Szenenrekonstruktion. Um Messungen der einzelnen Kameras sinnvoll zusammenzufĂŒhren, mĂŒssen die Parameter der Koordinatentransformationen zwischen den Kamerakoordinatensystemen möglichst exakt bestimmt werden. Klassische Methoden zur extrinsischen Kamerakalibrierung basieren in der Regel auf rĂ€umlichen Korrespondenzen zwischen Kamerabildern, was ein ĂŒberlappendes Sichtfeld voraussetzt. In dieser Arbeit werden alternative Methoden zur Lagebestimmung von Kameras innerhalb eines Mehrkamerasystems untersucht, die auf der Hand-Auge-Kalibrierung basieren und Zwangsbedingungen starr-gekoppelter Bewegung ausnutzen. Das Problem soll dabei im Wesentlichen anhand von Bilddaten gelöst werden, also unter Verzicht auf zusĂ€tzliche Inertialsensoren oder odometrische Daten. Die daraus abgeleiteten extrinsischen Kalibrierverfahren werden in Anlehnung an die Hand-Auge-Kalibrierung als Eye-to-Eye Calibration bezeichnet. Es werden Lösungsverfahren vorgestellt, die ausschlieĂlich auf Posemessdaten basieren und den Prozess der PoseschĂ€tzung von der eigentlichen Kalibrierung entkoppeln, sowie Erweiterungen, die direkt auf visuellen Informationen der einzelnen Kameras basieren. Die beschriebenen AnsĂ€tze fĂŒhren zu dem Entwurf eines Structure-from-Motion-Verfahrens, das PoseschĂ€tzung, Rekonstruktion der Szenengeometrie und extrinsische Kalibrierung der Kameras integriert. Bewegungskonfigurationen, die zu SingularitĂ€ten in den Kopplungsgleichungen fĂŒhren, werden gesondert analysiert und es werden spezielle Lösungsstrategien zur partiellen Kalibrierung fĂŒr solche FĂ€lle entworfen. Ein Schwerpunkt liegt hier auf Bewegung in der Ebene, da diese besonders hĂ€ufig in Anwendungsszenarien auftritt, in denen sich das Kamerasystem in oder auf einem Fahrzeug befindet
Two-Stage Transfer Learning for Heterogeneous Robot Detection and 3D Joint Position Estimation in a 2D Camera Image using CNN
Collaborative robots are becoming more common on factory floors as well as
regular environments, however, their safety still is not a fully solved issue.
Collision detection does not always perform as expected and collision avoidance
is still an active research area. Collision avoidance works well for fixed
robot-camera setups, however, if they are shifted around, Eye-to-Hand
calibration becomes invalid making it difficult to accurately run many of the
existing collision avoidance algorithms. We approach the problem by presenting
a stand-alone system capable of detecting the robot and estimating its
position, including individual joints, by using a simple 2D colour image as an
input, where no Eye-to-Hand calibration is needed. As an extension of previous
work, a two-stage transfer learning approach is used to re-train a
multi-objective convolutional neural network (CNN) to allow it to be used with
heterogeneous robot arms. Our method is capable of detecting the robot in
real-time and new robot types can be added by having significantly smaller
training datasets compared to the requirements of a fully trained network. We
present data collection approach, the structure of the multi-objective CNN, the
two-stage transfer learning training and test results by using real robots from
Universal Robots, Kuka, and Franka Emika. Eventually, we analyse possible
application areas of our method together with the possible improvements.Comment: 6+n pages, ICRA 2019 submissio
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
Extrinisic Calibration of a Camera-Arm System Through Rotation Identification
Determining extrinsic calibration parameters is a necessity in any robotic
system composed of actuators and cameras. Once a system is outside the lab
environment, parameters must be determined without relying on outside artifacts
such as calibration targets. We propose a method that relies on structured
motion of an observed arm to recover extrinsic calibration parameters. Our
method combines known arm kinematics with observations of conics in the image
plane to calculate maximum-likelihood estimates for calibration extrinsics.
This method is validated in simulation and tested against a real-world model,
yielding results consistent with ruler-based estimates. Our method shows
promise for estimating the pose of a camera relative to an articulated arm's
end effector without requiring tedious measurements or external artifacts.
Index Terms: robotics, hand-eye problem, self-calibration, structure from
motio
HeadOn: Real-time Reenactment of Human Portrait Videos
We propose HeadOn, the first real-time source-to-target reenactment approach
for complete human portrait videos that enables transfer of torso and head
motion, face expression, and eye gaze. Given a short RGB-D video of the target
actor, we automatically construct a personalized geometry proxy that embeds a
parametric head, eye, and kinematic torso model. A novel real-time reenactment
algorithm employs this proxy to photo-realistically map the captured motion
from the source actor to the target actor. On top of the coarse geometric
proxy, we propose a video-based rendering technique that composites the
modified target portrait video via view- and pose-dependent texturing, and
creates photo-realistic imagery of the target actor under novel torso and head
poses, facial expressions, and gaze directions. To this end, we propose a
robust tracking of the face and torso of the source actor. We extensively
evaluate our approach and show significant improvements in enabling much
greater flexibility in creating realistic reenacted output videos.Comment: Video: https://www.youtube.com/watch?v=7Dg49wv2c_g Presented at
Siggraph'1
Unobtrusive and pervasive video-based eye-gaze tracking
Eye-gaze tracking has long been considered a desktop technology that finds its use inside the traditional office setting, where the operating conditions may be controlled. Nonetheless, recent advancements in mobile technology and a growing interest in capturing natural human behaviour have motivated an emerging interest in tracking eye movements within unconstrained real-life conditions, referred to as pervasive eye-gaze tracking. This critical review focuses on emerging passive and unobtrusive video-based eye-gaze tracking methods in recent literature, with the aim to identify different research avenues that are being followed in response to the challenges of pervasive eye-gaze tracking. Different eye-gaze tracking approaches are discussed in order to bring out their strengths and weaknesses, and to identify any limitations, within the context of pervasive eye-gaze tracking, that have yet to be considered by the computer vision community.peer-reviewe
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
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