1,720,966 research outputs found
A non-parametric Calibration Algorithm for Depth Sensors Exploiting RGB Cameras
Full text linkRange sensors are common devices on modern robotic platforms. They endow
the robot with information about distance and shape of the objects in the sensors
field of view. In particular, the advent in the last few years of consumer RGB-
D sensors such as the Microsoft Kinect, has greatly fostered the development of
depth-based algorithms for robotics. In fact, such sensors can provide a large
quantity of data at a relatively low price.
In this thesis three different calibration problems for depth sensors are tackled.
The first original contribution to the state of the art is an algorithm to recover the
axis of rotation of a 2D laser range finder (LRF) mounted on a rotating support.
The key difference with other approaches is the use of kinematics point-plane
constraints to estimate the pose of the LRF with respect to a static camera, and
screw decomposition to recover the axis of rotation. The correct reconstruction of
a small indoor environment after calibration validates the proposed algorithm.
The second and most important original contribution of the thesis is a fully
automatic two-steps calibration algorithm for structured-light depth sensors (e.g.
Kinect). The key novelty of this work is the separation of the depth error into two
components, corrected with functions estimated on a pixel-basis. This separation,
validated by experimental observations, allows to dramatically reduce the number
of parameters in the final non-linear minimization and, consequently, the time for
the solution to converge to the global minimum. The depth images of a test set
corrected using the obtained calibration parameters are analyzed and compared to
the ground truth. The comparison shows that they differ from the real ones just
for an unpredictable noise. A qualitative analysis of the fusion between depth and
RGB data further confirms the effectiveness of the approach. Moreover, a ROS
package for both calibrating and correcting the Kinect data has been released as
open source.
The third contribution reported in the thesis is a new distributed calibration
algorithm for networks composed by cameras and already-calibrated depth sensors.
A ROS package implementing the proposed approach has been developed and
is available for free as a part of a big open source project for people tracking:
OpenPTrack. The developed package is able to calibrate networks composed by a
dozen sensors in real-time (i.e., batch processing is not needed), exploiting plane-
to-plane constraints and non-linear least squares optimization
Calibration of a Rotating 2D Laser Range Finder using Point-Plane Constraints
No full textA common method used to obtain 3D range data with a 2D laser range finder is to rotate the sensor. To combine the 2D range data obtained at different rotation angles into a common 3D coordinate frame, the axis of rotation rela- tive to the mirror center of the laser range finder should be known. This axis of rotation is a line in 3D space with four degrees of freedom. This paper describes a method for re- covering the parameters of this rotational axis, as well as the extrinsic calibration between the rotational axis and a camera. It simply requires scanning several planar checker- board patterns that are also imaged by a static camera. In particular, we use only correspondences between lines in the laser scans and planes in the camera images, which can be established easily even for non-visible lasers. Further- more, we show that such line-on-plane correspondences can be modelled as point-plane constraints, a problem studied in the field of robot kinematics. We use a numerical solution developed for such point-plane constraint prob- lems to obtain an initial estimate, which is then refined by a nonlinear minimization that minimizes the “line-of- sight” errors in the laser scans and the reprojection errors in the camera image. To validate our proposed method, we give experimental results using a LMS-100 mounted on a pan-tilt device in a nodding configuration
OpenPTrack: Open Source Multi-Camera Calibration and People Tracking for RGB-D Camera Networks
Full text linkOpenPTrack is an open source software for multi-camera calibration and people tracking in RGB-D camera networks. It allows to track people in big volumes at sensor frame rate and currently supports a heterogeneous set of 3D sensors.
In this work, we describe its user-friendly calibration procedure, which consists of simple steps with real-time feedback that allow to obtain accurate results in estimating the camera poses that are then used for tracking people. On top of a calibration based on moving a checkerboard within the tracking space and on a global optimization of cameras and checkerboards poses, a novel procedure which aligns people detections coming from all sensors in a x-y-time space is used for refining camera poses.
While people detection is executed locally, in the machines connected to each sensor, tracking is performed by a single node which takes into account detections from all over the network. Here we detail how a cascade of algorithms working on depth point clouds and color, infrared and disparity images is used to perform people detection from different types of sensors and in any indoor light condition.
We present experiments showing that a considerable improvement can be obtained with the proposed calibration refinement procedure that exploits people detections and we compare Kinect v1, Kinect v2 and Mesa SR4500 performance for people tracking applications. OpenPTrack is based on the Robot Operating System and the Point Cloud Library and has already been adopted in networks composed of up to ten imagers for interactive arts, education, culture and human–robot interaction applications
Robust Intrinsic and Extrinsic Calibration of RGB-D Cameras
Full text linkColor-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 three-dimensional (3-D) environment reconstruction and mapping, high precision object recognition, localization, etc.]. 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 3-D 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 robot operating system robotics framework. We report detailed experimental validations and performance comparisons to support our statements
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
A Distributed Calibration Algorithm for Color and Range Camera Networks
Full text linkIn this tutorial chapter we present a package to calibrate multi-device vision systems such as camera networks or robots. The proposed approach is able to estimate – in a unique and consistent reference frame – the rigid displacements of all the sensors in a network of standard cameras, Kinect-like depth sensors and Time-of-Flight range sensors. The sensor poses can be estimated in a few minutes with a user-friendly procedure: the user is only asked to move a checkerboard around while the ROS nodes acquire the data and perform the calibration. To make the system scalable, the data analysis is distributed in the network. This results in a low bandwidth usage as well as a really fast calibration procedure.
The ROS package is available on GitHub within the repository iaslab-unipd/calibration_toolkit1. The package has been developed for ROS Indigo in C++11 and Python, and tested on PCs equipped with Ubuntu 14.04 64bit
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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