Visual Perception for Manipulation and Imitation in Humanoid Robots

This thesis deals with visual perception for manipulation and imitation in humanoid robots. In particular, real-time applicable methods for object recognition and pose estimation as well as for markerless human motion capture have been developed. As only sensor a small baseline stereo camera system (approx. human eye distance) was used. An extensive experimental evaluation has been performed on simulated as well as real image data from real-world scenarios using the humanoid robot ARMAR-III

Numerical study of paraffin heat transfer in melting process in solar water heater storage tank

In this paper, we have studied paraffin heat transfer in melting process in solar water heater storage tanks by numerical investigation. We have used Gambit software for geometrical production. The output of this software has been used as the input for ANSYS Fluent. We have also used SIMPLE algorithm for solving algebraic equations. As for momentum equations we have used second-order upstream discretion and for other alternatives including kinetic energy and turbulence loss and finite volume transfer equation we have used first-order upstream discretion. The results show that the average heat goes up by the increase in the number of fins and reduces the melting time of phase change material (PCM). Also with the increase in the length of the fins more quantity of the material changes phases and turns into liquid. With the increase in thickness of the fins, minimal increase is seen in phase change of the material and its liquidation

Toward an Unified Representation for Imitation of Human Motion on Humanoids

Abstract — In this paper, we present a framework for perception, visualization, reproduction and recognition of human motion. On the perception side, various human motion capture systems exist, all of them having in common to calculate a sequence of configuration vectors for the human model in the core of the system. These human models may be 2D or 3D kinematic models, or on a lower level, 2D or 3D positions of markers. However, for appropriate visualization in terms of a 3D animation, and for reproduction on an actual robot, the acquired motion must be mapped to the target 3D kinematic model. On the understanding side, various action and activity recognition systems exist, which assume input of different kinds. However, given human motion capture data in terms of a high-dimensional 3D kinematic model, it is possible to transform the configurations into the appropriate representation which is specific to the recognition module. We will propose a complete architecture, allowing the replacement of any perception, visualization, reproduction module, or target platform. In the core of our architecture, we define a reference 3D kinematic model, which we intend to become a common standard in the robotics community, to allow sharing different software modules and having common benchmarks. I

Accurate Shape-Based 6-DoF Pose Estimation of Single-Colored Objects,” The 2009

Abstract-The problem of accurate 6-DoF pose estimation of 3D objects based on their shape has so far been solved only for specific object geometries. Edge-based recognition and tracking methods rely on the extraction of straight line segments or other primitives. Straight-forward extensions of 2D approaches are potentially more general, but assume a limited range of possible view angles. The general problem is that a 3D object can potentially produce completely different 2D projections depending on the view angle. One way to tackle this problem is to use canonical views. However, accurate shapebased 6-DoF pose estimation requires more information than matching of canonical views can provide. In this paper, we present a novel approach to 6-DoF pose estimation of singlecolored objects based on their shape. Our approach combines stereo triangulation with matching against a high-resolution view set of the object, each view having associated orientation information. The errors that arise from separating the position and orientation computation in first place are corrected by a subsequent correction procedure based on online 3D model projection. The proposed approach can estimate the pose of a single object within 20 ms using conventional hardware

Accurate Shape-Based 6-DoF Pose Estimation of Single-Colored Objects,” The 2009

Abstract-The problem of accurate 6-DoF pose estimation of 3D objects based on their shape has so far been solved only for specific object geometries. Edge-based recognition and tracking methods rely on the extraction of straight line segments or other primitives. Straight-forward extensions of 2D approaches are potentially more general, but assume a limited range of possible view angles. The general problem is that a 3D object can potentially produce completely different 2D projections depending on the view angle. One way to tackle this problem is to use canonical views. However, accurate shapebased 6-DoF pose estimation requires more information than matching of canonical views can provide. In this paper, we present a novel approach to 6-DoF pose estimation of singlecolored objects based on their shape. Our approach combines stereo triangulation with matching against a high-resolution view set of the object, each view having associated orientation information. The errors that arise from separating the position and orientation computation in first place are corrected by a subsequent correction procedure based on online 3D model projection. The proposed approach can estimate the pose of a single object within 20 ms using conventional hardware

Stereo-based markerless human motion capture for humanoid robot systems

Abstract-In this paper, we present an image-based markerless human motion capture system, intended for humanoid robot systems. The restrictions set by this ambitious goal are numerous. The input of the system is a sequence of stereo image pairs only, captured by cameras positioned at approximately eye distance. No artificial markers can be used to simplify the estimation problem. Furthermore, the complexity of all algorithms incorporated must be suitable for real-time application, which is maybe the biggest problem when considering the high dimensionality of the search space. Finally, the system must not depend on a static camera setup and has to find the initial configuration automatically. We present a system, which tackles these problems by combining multiple cues within a particle filter framework, allowing the system to recover from wrong estimations in a natural way. We make extensive use of the benefit of having a calibrated stereo setup. To reduce search space implicitly, we use the 3D positions of the hands and the head, computed by a separate hand and head tracker using a linear motion model for each entity to be tracked. With stereo input image sequences at a resolution of 320×240 pixels, the processing rate of our system is 15 Hz on a 3 GHz CPU. Experimental results documenting the performance of our system are available in form of several videos