187 research outputs found
Enabling Depth-driven Visual Attention on the iCub Humanoid Robot: Instructions for Use and New Perspectives
The importance of depth perception in the interactions that humans have
within their nearby space is a well established fact. Consequently, it is also
well known that the possibility of exploiting good stereo information would
ease and, in many cases, enable, a large variety of attentional and interactive
behaviors on humanoid robotic platforms. However, the difficulty of computing
real-time and robust binocular disparity maps from moving stereo cameras often
prevents from relying on this kind of cue to visually guide robots' attention
and actions in real-world scenarios. The contribution of this paper is
two-fold: first, we show that the Efficient Large-scale Stereo Matching
algorithm (ELAS) by A. Geiger et al. 2010 for computation of the disparity map
is well suited to be used on a humanoid robotic platform as the iCub robot;
second, we show how, provided with a fast and reliable stereo system,
implementing relatively challenging visual behaviors in natural settings can
require much less effort. As a case of study we consider the common situation
where the robot is asked to focus the attention on one object close in the
scene, showing how a simple but effective disparity-based segmentation solves
the problem in this case. Indeed this example paves the way to a variety of
other similar applications
Pedestrian detection and tracking using stereo vision techniques
Automated pedestrian detection, counting and tracking has received significant attention from the computer vision community of late. Many of the person detection techniques described so far in the literature work well in controlled environments, such as laboratory settings with a small number of people. This allows various assumptions to be made that simplify this complex problem. The performance of these techniques, however, tends to deteriorate when presented with unconstrained environments where pedestrian appearances, numbers, orientations, movements, occlusions and lighting conditions violate these convenient assumptions. Recently, 3D stereo information has been proposed as a technique to overcome some of these issues and to guide pedestrian detection. This thesis presents such an approach, whereby after obtaining robust 3D information via a novel disparity estimation technique, pedestrian detection is performed via a 3D point clustering process within a region-growing framework. This clustering process avoids using hard thresholds by using bio-metrically inspired constraints and a number of plan view statistics. This pedestrian detection technique requires no external training and is able to robustly handle challenging real-world unconstrained environments from various camera positions and orientations. In addition, this thesis presents a continuous detect-and-track approach, with additional kinematic constraints and explicit occlusion analysis, to obtain robust temporal tracking of pedestrians over
time. These approaches are experimentally validated using challenging datasets consisting of both synthetic data and real-world sequences gathered from a number of environments. In each case, the techniques are evaluated using both 2D and 3D groundtruth methodologies
Artificial Vision in the Nao Humanoid Robot
Projecte Final de Mà ster UPC realitzat en col.laboració amb l'Universitat Rovira i Virgili. Departament d'Enginyeria Informà tica i Matemà tiquesRobocup is an international robotic soccer competition held yearly to promote
innovative research and application in robotic intelligence. Nao humanoid robot
is the new RoboCup Standard Platform robot. This platform is the new Nao
robot designed and manufactured by the french company Aldebaran Robotics.
The new robot is an advanced platform for developing new computer vision and
robotics methods. This Master Thesis is oriented to the study of some fundamental
issues for the artificial vision in the Nao humanoid robots. In particular,
color representation models, real-time segmentation techniques, object detection
and visual sonar approaches are the computer vision techniques applied to Nao
robot in this Master Thesis. Also, Nao’s camera model, mathematical robot
kinematic and stereo-vision techniques are studied and developed. This thesis
also studies the integration between kinematic model and robot perception
model to perform RoboCup soccer games and RoboCup technical challenges.
This work is focused in the RoboCup environment but all computer vision and
robotics algorithms can be easily extended to another robotics fields
- …