This project presents a new display concept, which brings together haptics, augmented and mixed reality and tangible computing within the context of an intuitive conceptual design environment. The project extends the paradigm of augmented prototyping by allowing modelling of virtual geometry on the physical prototype, which can be touched by means of a haptic device. Wireless tracking of the physical prototype is achieved in three different ways by attaching to it a 'Speck', a tracker and Nintendo Wii Remote and it provides continuous tangible interaction. The physical prototype becomes a tangible interface augmented with mixed reality and with a novel 3D haptic design system.

In this article, we propose a new approach to the map building task: the implementation of the Spatial Semantic Hierarchy (SSH), proposed by B. Kuipers, on a real robot fitted with an omnidirectional camera. The original Kuiper's formulation of the SSH was slightly modified, in order to manage in a more efficient way the knowledge the real robot collects while moving in the environment. The sensory data experienced by the robot are transformed by the different levels of the SSH in order to obtain a compact representation of the environment. This knowledge is stored in the form of a topological map and, eventually, of a metrical map. The aim of this article is to show that a catadioptric omnidirectional camera is a good sensor for the SSH and nicely couples with several elements of the SSH. The panoramic view and rotational invariance of our omnidirectional camera makes the identification and labelling of places a simple matter. A deeper insight is that the tracking and identification of events on an omnidirectional image such as occlusions and alignments can be used for the segmentation of continuous sensory image data into the discrete topological and metric elements of a map. The proposed combination of the SSH and omnidirectional vision provides a powerful general framework for robot maping and offers new insights into the concept of “place.” Some preliminary experiments performed with a real robot in an unmodified office environment are presented.

Reports the steps undertaken in our work aimed to demonstrate the effectiveness of an omnidirectional vision sensor when conjugated with the spatial semantic hierarchy. The spatial semantic hierarchy was proposed by Kuipers (2000) as a method for map building with robots. In our work, a robot builds a topological map of an unknown environment, using the spatial semantic hierarchy and an omnidirectional vision system as the only sensor. We present a new omnidirectional mirror and a new robot. The new mirror was expressly designed for this application, the robot's chassis was designed to create a synergy with the omnidirectional vision sensor. A complete description of our project is reported, underlying the strict link it is possible to create between omnidirectional vision and the spatial semantic hierarchy. Experiments in simulated environments and in real environments produced positive results.

Many of the pruning strategies used to remove less likely hypotheses from the search beam in large vocabulary speech recognition (LVR) systems, have a peak search space many times greater than the average search space. This paper discusses two such pruning strategies used within BT’s speech recognition architecture [1], Step pruning and Histogram pruning. Two-tier pruning is proposed as a simple but powerful extension applicable to either of the above strategies. This seeks to limit the expansion of the search space between the prune and acoustic match processes without affecting accuracy. It is shown that the application of two-tier pruning to either strategy reduces peak search effort, and results in an average reduction in run time of 33% and 53% for step pruning and histogram pruning respectively, with no loss in top-N accuracy.