A Proposal for Semantic Map Representation and Evaluation

Semantic mapping is the incremental process of “mapping” relevant information of the world (i.e., spatial information, temporal events, agents and actions) to a formal description supported by a reasoning engine. Current research focuses on learning the semantic of environments based on their spatial location, geometry and appearance. Many methods to tackle this problem have been proposed, but the lack of a uniform representation, as well as standard benchmarking suites, prevents their direct comparison. In this paper, we propose a standardization in the representation of semantic maps, by defining an easily extensible formalism to be used on top of metric maps of the environments. Based on this, we describe the procedure to build a dataset (based on real sensor data) for benchmarking semantic mapping techniques, also hypothesizing some possible evaluation metrics. Nevertheless, by providing a tool for the construction of a semantic map ground truth, we aim at the contribution of the scientific community in acquiring data for populating the dataset

Dynamic urban projection mapping

“Dynamic projection mapping” is a variation of the best-known “projection mapping”. It considers the perceptual analysis of the urban landscape in which the video projection and the observer’s displacement speed are hypothesized. This latter, in particular, is variable and may depend on factors not directly controllable by the driver (slowdowns due to accidents, rallies, etc.). This speed can be supported and controlled by a number of traffic flow measurement systems. These data are available on the internet, like Google Maps APIs and/or speed sensors located close to the point of interest. The content of projection becomes dynamic and varies according to how the observer perceives the vehicle: slow, medium, fast

What is the object of the encapsulation of a process?

Several theories have been proposed to describe the transition from process to object in mathematical thinking. Yet, what is the nature of this ''object'' produced by the ''encapsulation'' of a process? Here, we outline the development of some of the theories (including Piaget, Dienes, Davis, Greeno, Dubinsky, Sfard, Gray, and Tall) and consider the nature of the mental objects (apparently) produced through encapsulation and their role in the wider development of mathematical thinking. Does the same developmental route occur in geometry as in arithmetic and algebra? Is the same development used in axiomatic mathematics? What is the role played by imagery

Investigations of the carbon fibre cross-sectional areas and their non-circularities by means of laser diffraction

Laser diffraction is a commonly used tool to measure the fibre diameter of carbon fibres prior to mechanical testing. However, non-circularities of carbon fibres need to be considered in order to minimise measuring errors. As the work at hand demonstrates, using a single measurement of the fibre diameter may cause deviations as high as 30% from a computationally determined value. It appears that the error can be minimised by acquiring a data set of several apparent diameters as a function of the angle around the fibre axis. Based on this data, the cross-sectional area can be calculated as a circle with an averaged diameter or as an ellipse by applying an elliptical fitting procedure

Near Earth Space Object Detection Utilizing Parallax as Multi-hypothesis Test Criterion

The US Strategic Command (USSTRATCOM) operated Space Surveillance Network (SSN) is tasked with Space Situational Awareness (SSA) for the US military. This system is made up of Electro-Optic sensors such as the Space Surveillance Telescope (SST) and Ground-based Electro-Optical Deep Space Surveillance (GEODSS) as well as RADAR based sensors such as the Space Fence. While Lockheed Martin’s Space Fence is very adept at detecting & tracking objects in Low Earth Orbit (LEO) below 3000 Km in height [1], gaps remain in the tracking of Resident Space Objects (RSO’s) in Geosynchronous Orbits (GEO) due to limitations associated with the implementation of the SST and GEODSS systems. This thesis explores a reliable, ground-based technique to quickly determine the altitude of a RSO from a single or limited set of observations; implementation of such sensors into the SSN would mitigate GEO SSA performance gaps. The research entails a method to distinguish between the point spread function (PSF) observed by a star and the PSF observed from an RSO using Multi-Hypothesis Testing with parallax as a test criterion. Parallax is the effect that an observed object will appear to shift when viewed from different positions. This effect is explored by generating PSFs from telescope observations of space objects at different baselines. The research has shown the PSF of an RSO can be distinguished from that of a star using single, simultaneous observations from reference and parallax sensing telescopes. This thesis validates these techniques with both simulations and with experimental data from the SST and Naval Observatory sensors