In this paper, we present an algorithm that accelerates 3D texture-based volume rendering of large, sparse data sets, i.e., data sets where only a fraction of the voxels contain relevant information. In texture-based approaches, the rendering performance is affected by the fill-rate, the size of texture memory, and the texture I/O bandwidth. For sparse data, these limitations can be circumvented by restricting most of the rendering work to the relevant parts of the volume. In order to efficiently enclose the corresponding regions with axis-aligned boxes, we employ a hierarchical data structure, known as an AMR (Adaptive Mesh Refinement) tree. The hierarchy is generated utilizing a clustering algorithm. A good balance is thereby achieved between the size of the enclosed volume, i.e., the amount to render in graphics hardware and the number of axis-aligned regions, i.e., the number of texture coordinates to compute in software. The waste of texture memory by the power-of-two restriction is minimized by a 3D packing algorithm which arranges texture bricks economically in memory. Compared to an octree approach, the rendering performance is significantly increased and less parameter tuning is necessary.
Abstract. This paper describes a method to reduce the effects of the system immanent delay when tracking and controlling fast moving robots using a fixed video camera as sensor. The robots are driven by a computer with access to the video signal. The paper explains how we cope with system latency by predicting the movement of our robots using linear models and neural networks. We use past positions and orientations of the robot for the prediction, as well as the most recent commands sent. The setting used for our experiments is the same used in the small-size league of the RoboCup competition. We have successfully field-tested our predictors at several RoboCup events with our FU-Fighters team. Our results show that path prediction can significantly improve speed and accuracy of robotic play.
Abstract. This paper describes the mechanical and electrical design, as well as the control strategy, of the FU-Fighters robots, a F180 league team that won the second place at RoboCup'99. It explains how we solved the computer vision and radio communication problems that arose in the course of the project. The paper mainly discusses the hierarchical control architecture used to generate the behavior of individual agents and the team. Our reactive approach is based on the Dual Dynamics framework developed by H. Jäger, in which activation dynamics determines when a behavior is allowed to influence the actuators, and a target dynamics establishes how this is done. We extended the original framework by adding a third module, the perceptual dynamics. Here, the readings of fast changing sensors are aggregated temporarily to form complex, slow changing percepts. We describe the bottom-up design of behaviors and illustrate our approach using examples from the RoboCup domain.
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