13,028 research outputs found
Real-time marker-less multi-person 3D pose estimation in RGB-Depth camera networks
This paper proposes a novel system to estimate and track the 3D poses of
multiple persons in calibrated RGB-Depth camera networks. The multi-view 3D
pose of each person is computed by a central node which receives the
single-view outcomes from each camera of the network. Each single-view outcome
is computed by using a CNN for 2D pose estimation and extending the resulting
skeletons to 3D by means of the sensor depth. The proposed system is
marker-less, multi-person, independent of background and does not make any
assumption on people appearance and initial pose. The system provides real-time
outcomes, thus being perfectly suited for applications requiring user
interaction. Experimental results show the effectiveness of this work with
respect to a baseline multi-view approach in different scenarios. To foster
research and applications based on this work, we released the source code in
OpenPTrack, an open source project for RGB-D people tracking.Comment: Submitted to the 2018 IEEE International Conference on Robotics and
Automatio
Advancing automation and robotics technology for the space station and for the US economy: Submitted to the United States Congress October 1, 1987
In April 1985, as required by Public Law 98-371, the NASA Advanced Technology Advisory Committee (ATAC) reported to Congress the results of its studies on advanced automation and robotics technology for use on the space station. This material was documented in the initial report (NASA Technical Memorandum 87566). A further requirement of the Law was that ATAC follow NASA's progress in this area and report to Congress semiannually. This report is the fifth in a series of progress updates and covers the period between 16 May 1987 and 30 September 1987. NASA has accepted the basic recommendations of ATAC for its space station efforts. ATAC and NASA agree that the mandate of Congress is that an advanced automation and robotics technology be built to support an evolutionary space station program and serve as a highly visible stimulator affecting the long-term U.S. economy
Markerless visual servoing on unknown objects for humanoid robot platforms
To precisely reach for an object with a humanoid robot, it is of central
importance to have good knowledge of both end-effector, object pose and shape.
In this work we propose a framework for markerless visual servoing on unknown
objects, which is divided in four main parts: I) a least-squares minimization
problem is formulated to find the volume of the object graspable by the robot's
hand using its stereo vision; II) a recursive Bayesian filtering technique,
based on Sequential Monte Carlo (SMC) filtering, estimates the 6D pose
(position and orientation) of the robot's end-effector without the use of
markers; III) a nonlinear constrained optimization problem is formulated to
compute the desired graspable pose about the object; IV) an image-based visual
servo control commands the robot's end-effector toward the desired pose. We
demonstrate effectiveness and robustness of our approach with extensive
experiments on the iCub humanoid robot platform, achieving real-time
computation, smooth trajectories and sub-pixel precisions
- …