Visual navigation is a ubiquitous yet complex task which is performed by many species for the purpose of survival. Although visual navigation is actively being studied within the robotics community, the determination of elemental constituents of a robust visual navigation system remains a challenge. Motion estimation is mistakenly considered as the sole ingredient to make a robust autonomous visual navigation system and therefore efforts are made to improve the accuracy of motion estimations. On the contrary, there are other factors which are as important as motion and whose absence could result in inability to perform seamless visual navigation such as the one exhibited by humans. Therefore, it is needed that a general model for a visual navigation system be devised which would describe it in terms of a set of elemental units. In this regard, a set of visual navigation elements (i.e. spatial memory, motion memory, scene geometry, context and scene semantics) are suggested as building blocks of a visual navigation system in this thesis. A set of methods are proposed which investigate the existence and role of visual navigation elements in a visual navigation system. A quantitative research methodology in the form of a series of systematic experiments is conducted on these methods. The thesis formulates, implements and analyzes the proposed methods in the context of visual navigation elements which are arranged into three major groupings; a) Spatial memory b) Motion Memory c) Manhattan, context and scene semantics. The investigations are carried out on multiple image datasets obtained by robot mounted cameras (2D/3D) moving in different environments. Spatial memory is investigated by evaluation of proposed place recognition methods. The recognized places and inter-place associations are then used to represent a visited set of places in the form of a topological map. Such a representation of places and their spatial associations models the concept of spatial memory. It resembles the humans’ ability of place representation and mapping for large environments (e.g. cities). Motion memory in a visual navigation system is analyzed by a thorough investigation of various motion estimation methods. This leads to proposals of direct motion estimation methods which compute accurate motion estimates by basing the estimation process on dominant surfaces. In everyday world, planar surfaces, especially the ground planes, are ubiquitous. Therefore, motion models are built upon this constraint. Manhattan structure provides geometrical cues which are helpful in solving navigation problems. There are some unique geometric primitives (e.g. planes) which make up an indoor environment. Therefore, a plane detection method is proposed as a result of investigations performed on scene structure. The method uses supervised learning to successfully classify the segmented clusters in 3D point-cloud datasets. In addition to geometry, the context of a scene also plays an important role in robustness of a visual navigation system. The context in which navigation is being performed imposes a set of constraints on objects and sections of the scene. The enforcement of such constraints enables the observer to robustly segment the scene and to classify various objects in the scene. A contextually aware scene segmentation method is proposed which classifies the image of a scene into a set of geometric classes. The geometric classes are sufficient for most of the navigation tasks. However, in order to facilitate the cognitive visual decision making process, the scene ought to be semantically segmented. The semantic of indoor scenes as well as semantic of the outdoor scenes are dealt with separately and separate methods are proposed for visual mapping of environments belonging to each type. An indoor scene consists of a corridor structure which is modeled as a cubic space in order to build a map of the environment. A “flash-n-extend” strategy is proposed which is responsible for controlling the map update frequency. The semantics of the outdoor scenes is also investigated and a scene classification method is proposed. The method employs a Markov Random Field (MRF) based classification framework which generates a set of semantic maps.

This thesis shows that the mapping of semantics to syntax can be more complex than is generally assumed. In general, the mapping of semantics to syntax is thought to be many-to-one; for instance, many types of external argument roles are mapped to a subject position, and a theme or patient role is mapped to an object position. Contrary to this view, I show, by studying the syntax and semantics of external arguments, that one-to-one mapping between syntax and semantics is possible. External arguments are generally assumed to be introduced by a functional head, called Voice or v, regardless of the semantics of the argument, rather than being actual arguments of the verbs. A high Appl head similar to Voice has recently been argued to introduce external arguments as well as arguments of other semantic types. At present, no theories propose how these heads are distinguished in argument structure. This thesis articulates the differences between the external argument introducing heads and explores the consequences of these differences. Moreover, this thesis proposes a new type of event-related applicative, namely peripheral Appl. Like Voice and high Appl, peripheral Appl introduces an argument external to the verb phrase. The key differences among the external argument introducing heads are in their semantics as well as their syntactic position. Semantically, Voice is specified for agentivity, but high and peripheral Appls are specified for non-agentivity. Syntactically, high Appl merges below Voice, not above, while peripheral Appl can merge above Voice. An important result emerging from this thesis is that not all external arguments are treated in the same way in syntax: not only are agent and non-agent external argument roles mapped into different positions, but different types of non-agent roles are also mapped into different positions.

This thesis shows that the mapping of semantics to syntax can be more complex than is generally assumed. In general, the mapping of semantics to syntax is thought to be many-to-one; for instance, many types of external argument roles are mapped to a subject position, and a theme or patient role is mapped to an object position. Contrary to this view, I show, by studying the syntax and semantics of external arguments, that one-to-one mapping between syntax and semantics is possible. External arguments are generally assumed to be introduced by a functional head, called Voice or v, regardless of the semantics of the argument, rather than being actual arguments of the verbs. A high Appl head similar to Voice has recently been argued to introduce external arguments as well as arguments of other semantic types. At present, no theories propose how these heads are distinguished in argument structure. This thesis articulates the differences between the external argument introducing heads and explores the consequences of these differences. Moreover, this thesis proposes a new type of event-related applicative, namely peripheral Appl. Like Voice and high Appl, peripheral Appl introduces an argument external to the verb phrase. The key differences among the external argument introducing heads are in their semantics as well as their syntactic position. Semantically, Voice is specified for agentivity, but high and peripheral Appls are specified for non-agentivity. Syntactically, high Appl merges below Voice, not above, while peripheral Appl can merge above Voice. An important result emerging from this thesis is that not all external arguments are treated in the same way in syntax: not only are agent and non-agent external argument roles mapped into different positions, but different types of non-agent roles are also mapped into different positions.

In the future, robots will expand from industrial and research applications to the home. Domestic service robots will work in the home to perform useful tasks such as object retrieval, cleaning, organization, and security. The tireless support of these systems will not only enable able bodied people to avoid mundane chores; they will also enable the elderly to remain independent from institutional care by providing service, safety, and companionship. Robots will need to understand the relationship between objects and their environments to perform some of these tasks. Structured indoor environments are organized according to architectural guidelines and convenience for their residents. Utilizing this information makes it possible to predict the location of objects. Conversely, one can also predict the function of a room from the detection of a few objects within a given space. This thesis introduces a framework for combining object permanence and context called the probabilistic cognitive model. This framework combines reasoning about spatial extent of places and the identity of objects and their relationships to one another and to the locations where they appear. This type of reasoning takes into account the context in which objects appear to determine their identity and purpose. The probabilistic cognitive model combines a mapping system called OmniMapper with a conditional random field probabilistic model for context representation. The conditional random field models the dependencies between location and identity in a real-world domestic environment. This model is used by mobile robot systems to predict the effects of their actions during autonomous object search tasks in unknown environments.

Automatic marking of coursework has many advantages in terms of resource benefits and consistency. Diagrams are quite common in many domains including computer science but marking them automatically is a challenging task. There has been previous research to accomplish this, but results to date have been limited. Much of the meaning of a diagram is contained in the labels and in order to automatically mark the diagrams the labels need to be understood. However the choice of labels used by students in a diagram is largely unrestricted and diversity of labels can be a problem while matching. This thesis has measured the extent of the diagram label matching problem and proposed and evaluated a configurable extensible framework to solve it. A new hybrid syntax matching algorithm has also been proposed and evaluated. This hybrid approach is based on the multiple existing syntax algorithms. Experiments were conducted on a corpus of coursework which was large scale, realistic and representative of UK HEI students. The results show that the diagram label matching is a substantial problem and cannot be easily avoided for the e-assessment of diagrams. The results also show that the hybrid approach was better than the three existing syntax algorithms. The results also show that the framework has been effective but only to limited extent and needs to be further refined for the semantic stage. The framework proposed in this Thesis is configurable and extensible. It can be extended to include other algorithms and set of parameters. The framework uses configuration XML, dynamic loading of classes and two design patterns namely strategy design pattern and facade design pattern. A software prototype implementation of the framework has been developed in order to evaluate it. Finally this thesis also contributes the corpus of coursework and an open source software implementation of the proposed framework. Since the framework is configurable and extensible, its software implementation can be extended and used by the research community.

The purpose of this investigation was to research vocabulary strategies that could help improve student art vocabulary retention. The subjects were five intact 3rd grade classes at a culturally diverse elementary school outside of Washington, DC. The vocabulary strategies, concept wheel and semantic mapping, were modified and incorporated into a string printmaking unit for two of the five classes. The remaining three classes were taught the same printmaking unit, with the exclusion of the modified vocabulary activities. The results of a labeling assessment given to each class on three separate occasions indicate that the incorporation of vocabulary activities does help students retain art vocabulary. Possible modifications of multiple proven vocabulary strategies for use in an art classroom setting are discussed.

<p>In this thesis, semantic mapping is understood to be the process of putting a tag or label on objects or regions in a map. This label should be interpretable by and have a meaning for a human. The use of semantic information has several application areas in mobile robotics. The largest area is in human-robot interaction where the semantics is necessary for a common understanding between robot and human of the operational environment. Other areas include localization through connection of human spatial concepts to particular locations, improving 3D models of indoor and outdoor environments, and model validation.</p><p>This thesis investigates the extraction of semantic information for mobile robots in outdoor environments and the use of semantic information to link ground-level occupancy maps and aerial images. The thesis concentrates on three related issues: i) recognition of human spatial concepts in a scene, ii) the ability to incorporate semantic knowledge in a map, and iii) the ability to connect information collected by a mobile robot with information extracted from an aerial image.</p><p>The first issue deals with a vision-based virtual sensor for classification of views (images). The images are fed into a set of learned virtual sensors, where each virtual sensor is trained for classification of a particular type of human spatial concept. The virtual sensors are evaluated with images from both ordinary cameras and an omni-directional camera, showing robust properties that can cope with variations such as changing season.</p><p>In the second part a probabilistic semantic map is computed based on an occupancy grid map and the output from a virtual sensor. A local semantic map is built around the robot for each position where images have been acquired. This map is a grid map augmented with semantic information in the form of probabilities that the occupied grid cells belong to a particular class. The local maps are fused into a global probabilistic semantic map covering the area along the trajectory of the mobile robot.</p><p>In the third part information extracted from an aerial image is used to improve the mapping process. Region and object boundaries taken from the probabilistic semantic map are used to initialize segmentation of the aerial image. Algorithms for both local segmentation related to the borders and global segmentation of the entire aerial image, exemplified with the two classes ground and buildings, are presented. Ground-level semantic information allows focusing of the segmentation of the aerial image to desired classes and generation of a semantic map that covers a larger area than can be built using only the onboard sensors.</p>

Although much progress has been made in the field of robotic mapping, many challenges remain including: efficient semantic segmentation using RGB-D sensors, map representations that include complex features (structures and objects), and interfaces for interactive annotation of maps. This thesis addresses how prior knowledge of semi-structured human environments can be leveraged to improve segmentation, mapping, and semantic annotation of maps. We present an organized connected component approach for segmenting RGB-D data into planes and clusters. These segments serve as input to our mapping approach that utilizes them as planar landmarks and object landmarks for Simultaneous Localization and Mapping (SLAM), providing necessary information for service robot tasks and improving data association and loop closure. These features are meaningful to humans, enabling annotation of mapped features to establish common ground and simplifying tasking. A modular, open-source software framework, the OmniMapper, is also presented that allows a number of different sensors and features to be combined to generate a combined map representation, and enabling easy addition of new feature types.

We take a healthcare knowledge management approach to represent the Clinical Pathway (CP) as workflows. We have developed a semantic representation of CP in terms of a CP ontology that outlines the different clinical processes, their properties, constraints and relationships, and is able to computerize a range of CP. To model business workflows we use the graphical Business Process Modeling Notation (BPMN) modeling language that generates a BPMN ontology. To represent a CP as a BPMN workflow, we have developed a semantic interoperability (mapping ontology) framework between the CP ontology and the BPMN ontology. The mapping ontology allows the alignment of relations between two ontologies and ensures that a clinical process defined in the CP ontology is mapped to a standard BPMN workflow element. We execute our BPMN-based CP in the Lombardi workflow engine, whereby users can view the execution of the CP and make the necessary adjustments.

There are two significant impedances to the realization of the potential of ontologies. First, many ontology designers lack the necessary background in formal logics to express their intuitions clearly and precisely, resulting in the proliferation of ontologies with low expressivity. Concurrently, developing semantic mappings between existing ontologies is difficult, because much of the semantics is external to the representation. This thesis uses the idea of metaphor to develop architectures for ontology repositories to serve as bottom-up reusable resources. Moreover, an ontology design algorithm has been developed that allows designers to communicate their ideas at the semantic level, simply by generating and vetting models. Finally, a semantic mapping algorithm has been developed that uses an ontology repository to determine the similarities and differences between any number of target ontologies. An ontology for partial orders has been elaborated to demonstrate the proof of concept and populate the first iteration of the repository.