Catálogo de publicaciones - libros

In order to apply advanced high-level concepts for transportation networks, like hypergraphs, multi-level wayfinding and traffic forecasting, to commercially available street network datasets, it is often necessary to generalise from network primitives. However, the appropriate method of generalisation strongly depends on the complex street network feature they belong to. In this paper, we develop formal expressions for road segments and some essential types of roads, like roundabouts, dual carriageways and freeways. For this purpose, a formal network language is developed, which allows a clear distinction among the geometrical network, its embedding into the Euclidian plane, as well as navigational constraints for a traffic mode.

When animals (including humans) first explore a new environment, what they remember is fragmentary knowledge about the places visited. Yet, they have to use such fragmentary knowledge to find their way home. Humans naturally use more powerful heuristics while lower animals have shown to develop a variety of methods that tend to utilize two key pieces of information, namely distance and orientation information. Their methods differ depending on how they sense their environment. Could a mobile robot be used to investigate the nature of such a process, commonly referred to in the psychological literature as cognitive mapping? What might be computed in the initial explorations and how is the resulting “cognitive map” be used for localization? In this paper, we present an approach using a mobile robot to generate a “cognitive map”, the main focus being on experiments conducted in large spaces that the robot cannot apprehend at once due to the very limited range of its sensors. The robot computes a “cognitive map” and uses distance and orientation information for localization.

Probabilistic robot mapping techniques can produce high resolution, accurate maps of large indoor and outdoor environments. However, much less progress has been made towards robots using these maps to perform useful functions such as efficient navigation. This paper describes a pragmatic approach to mapping system development that considers not only the map but also the navigation functionality that the map must provide. We pursue this approach within a bio-inspired mapping context, and use results from robot experiments in indoor and outdoor environments to demonstrate its validity. The research attempts to stimulate new research directions in the field of robot mapping with a proposal for a new approach that has the potential to lead to more complete mapping and navigation systems.

The paper proposes a novel approach for a scale-dependent geometric simplification of 3D building models that are an integral part of virtual cities. In contrast to real-time photorealistic visualisations, map-like presentations emphasize the specific cartographic properties of objects. For buildings objects, such properties are e.g. the parallel and right-angled arrangements of facade walls and the symmetries of the roof structure. To a map, a clear visual perception of the spatial situation is more important than a detailed reflection of reality. Therefore, the simplification of a 3D building model must be the transformation of the object into its global shape. We present a two-stage algorithm for such an object-specific simplification, which combines primitive instancing and cell decomposition to recreate a basic building model that best fits the objects original shape.

Over-reliance on automated navigation systems may cause users to be “mindless” of the environment and not develop the spatial knowledge that maybe required when automation fails. This research focused on the potential degradation in spatial knowledge acquisition due to the reliance on automatic wayfinding systems. In addition, the impact of “keeping the user in the loop” strategies on spatial knowledge was examined. Participants performed wayfindings tasks in a virtual building with continuous or by-request position indication, in addition to responding to occasional orientation quizzes. Findings indicate that having position indication by request and orientation quizzes resulted in better acquired spatial knowledge. The findings are discussed in terms of keeping the user actively investing mental effort in the wayfinding task as a strategy to reduce the possible negative impact of automated navigation systems.

While navigation instructions in terms of turn instructions and distances are suitable for guiding drivers on roads, a different context of use - like pedestrian navigation - requires extended routing data and algorithms as well as adapted presentation forms to be effective. In our work we study alternative forms of navigation instructions for pedestrians in city environments. In this paper we explore the usefulness of directions given in the form of a short story. To aid retention of navigation instructions and recognition of decision points along the route we have expanded a landmark-based navigation system to present navigation instructions as a sequence of story elements. In this paper we introduce the concept of stories as route descriptions, describe the current prototype implementation, and present preliminary evaluation results from user tests that will guide further development.

The character of a landscape can be seen as the outcome of people’s perception of their physical environment, which is important for spatial planning and decision making. Three modes of landscape perception are proposed: view from a viewpoint, view from a road, and view of an area. Three sampling methods to calculate visibility measures simulate these modes of perception. We compared the results of the three sampling methods for two study areas. The ROPE method provides information about subspaces. The road method enables the analysis of sequences. The grid point method calculates visibility measures at almost every location in space, providing detailed information about transitions and pattern change between original and new situations. The mean visibility values for the study areas reveal major differences between the sampling methods. Combining the results of the three methods is expected to be useful for describing all the facets of landscape perception.

Semantic integrity constraints specify relations between entity classes. These relations must hold to ensure that the data conforms to the semantics intended by the data model. For spatial data many semantic integrity constraints are based on spatial properties like topological or metric relations. Reasoning on such spatial relations and the corresponding derivation of implicit knowledge allow for many interesting applications. The paper investigates reasoning algorithms which can be used to check the internal consistency of a set of spatial semantic integrity constraints. Since integrity constraints are defined at the class level, the logical properties of spatial relations can not directly be applied. Therefore a set of 17 abstract class relations has been defined, which combined with the instance relations enables the specification of integrity constraints. The investigated logical properties of the class relations enable to discover conflicts and redundancies in sets of spatial semantic integrity constraints.

Cavities in spatial phenomena require geometric representations of regions with holes. Existing models for reasoning over topological relations either exclude such specialized regions (9-intersection) or treat them indistinguishably from regions without holes (RCC-8). This paper highlights that inferences over a region with a hole need to be made separately from, and in addition to, the inferences over regions without holes. First the set of 23 topological relations between a region and a region with a hole is derived systematically. Then these relations’ compositions over the region with the hole are calculated so that the inferences can be compared with the compositions of the topological relations over regions without holes. For 266 out of the 529 compositions the results over the region with the hole were more detailed than the corresponding results over regions without holes, with 95 of these refined cases providing even a unique result. In 27 cases, this refinement up to uniqueness compares with a completely undetermined inference for the relations over regions without holes.

In this paper, we propose a postclustering process that robustly computes cluster regions at different levels of granularity through the complete Order- Voronoi diagrams. The robustness and flexibility of the proposed method overcome the application-dependency and rigidity of traditional approaches. The proposed cluster tessellation method robustly models monotonic and nonmonotonic cluster growth, and provides fuzzy membership in order to represent indeterminacy of cluster regions. It enables the user to explore cluster structures hidden in a dataset in various scenarios and supports “what-if” and “what-happen” analysis. Tessellated clusters can be effectively used for cluster reasoning and concept learning.