Qualitative distances and qualitative description of images for indoor scene description and recognition in robotics

This thesis is focused on reducing the gap between the acquisition of low level information by robot sensors and the need of obtaining high level information for enhancing human-machine communication and for applying logical reasoning processes. To this end, approaches for qualitative and semantic image description and qualitative distance sensor interpretation were developed. Experimentation was carried out on di↵erent robotic platforms showing useful applications

Qualitative Distances and Qualitative Description of Images for Indoor Scene Description and Recognition in Robotics

The automatic extraction of knowledge from the world by a robotic system as human beings interpret their environment through their senses is still an unsolved task in Artificial Intelligence. A robotic agent is in contact with the world through its sensors and other electronic components which obtain and process mainly numerical information. Sonar, infrared and laser sensors obtain distance information. Webcams obtain digital images that are represented internally as matrices of red, blue and green (RGB) colour coordinate values. All this numerical values obtained from the environment need a later interpretation in order to provide the knowledge required by the robotic agent in order to carry out a task. Similarly, light wavelengths with specific amplitude are captured by cone cells of human eyes obtaining also stimulus without meaning. However, the information that human beings can describe and remember from what they see is expressed using words, that is qualitatively. The research work done in this thesis tries to narrow the gap between the acquisition of low level information by robot sensors and the need of obtaining high level or qualitative information for enhancing human-machine communication and for applying logical reasoning processes based on concepts. Moreover, qualitative concepts can be added a meaning by relating them to others. They can be used for reasoning applying qualitative models that have been developed in the last twenty years for describing and interpreting metrical and mathematical concepts such as orientation, distance, velocity, acceleration, and so on. And they can be also understood by human-users both written and read aloud. The first contribution presented is the definition of a method for obtaining fuzzy distance patterns (which include qualitative distances such as near , far , very far and so on) from the data obtained by any kind of distance sensors incorporated in a mobile robot and the definition of a factor to measure the dissimilarity between those fuzzy patterns. Both have been applied to the integration of the distances obtained by the sonar and laser distance sensors incorporated in a Pioneer 2 dx mobile robot and, as a result, special obstacles have been detected as glass window , mirror , and so on. Moreover, the fuzzy distance patterns provided have been also defuzzified in order to obtain a smooth robot speed and used to classify orientation reference systems into open (it defines an open space to be explored) or closed . The second contribution presented is the definition of a model for qualitative image description (QID) based on qualitative models of shape, colour, topology and orientation. This model can qualitatively describe any kind of digital image and is independent of the image segmentation method used. The QID model have been tested in two scenarios in robotics: (i) the description of digital images captured by the camera of a Pioneer 2 dx mobile robot and (ii) the description of digital images of tile mosaics taken by an industrial camera located on a platform used by a robot arm to assemble tile mosaics. In order to provide a formal and explicit meaning to the qualitative description of the images generated, a Description Logic (DL) based ontology has been designed and presented as the third contribution. Our approach can automatically process any random image and obtain a set of DL-axioms that describe it visually and spatially. And objects included in the images are classified according to the ontology schema using a DL reasoner. Tests have been carried out using digital images captured by a webcam incorporated in a Pioneer 2 dx mobile robot. The images taken correspond to the corridors of a building at University Jaume I and objects with them have been classified into walls , floor , office doors and fire extinguishers under different illumination conditions and from different observer viewpoints. The final contribution is the definition of a similarity measure between qualitative descriptions of shape, colour, topology and orientation. And the integration of those measures into the definition of a general similarity measure between two qualitative descriptions of images. These similarity measures have been applied to: (i) extract objects with similar shapes from the MPEG7 CE Shape-1 library; (ii) assemble tile mosaics by qualitative shape and colour similarity matching; (iii) compare images of tile compositions; and (iv) compare images of natural landmarks in a mobile robot world for their recognition

Reasoning about topological and cardinal direction relations between 2-dimensional spatial objects

Increasing the expressiveness of qualitative spatial calculi is an essential step towards meeting the requirements of applications. This can be achieved by combining existing calculi in a way that we can express spatial information using relations from multiple calculi. The great challenge is to develop reasoning algorithms that are correct and complete when reasoning over the combined information. Previous work has mainly studied cases where the interaction between the combined calculi was small, or where one of the two calculi was very simple. In this paper we tackle the important combination of topological and directional information for extended spatial objects. We combine some of the best known calculi in qualitative spatial reasoning, the RCC8 algebra for representing topological information, and the Rectangle Algebra (RA) and the Cardinal Direction Calculus (CDC) for directional information. We consider two different interpretations of the RCC8 algebra, one uses a weak connectedness relation, the other uses a strong connectedness relation. In both interpretations, we show that reasoning with topological and directional information is decidable and remains in NP. Our computational complexity results unveil the significant differences between RA and CDC, and that between weak and strong RCC8 models. Take the combination of basic RCC8 and basic CDC constraints as an example: we show that the consistency problem is in P only when we use the strong RCC8 algebra and explicitly know the corresponding basic RA constraints

Spatial Relations and Natural-Language Semantics for Indoor Scenes

Over the past 15 years, there have been increased efforts to represent and communicate spatial information about entities within indoor environments. Automated annotation of information about indoor environments is needed for natural-language processing tasks, such as spatially anchoring events, tracking objects in motion, scene descriptions, and interpretation of thematic places in relationship to confirmed locations. Descriptions of indoor scenes often require a fine granularity of spatial information about the meaning of natural-language spatial utterances to improve human-computer interactions and applications for the retrieval of spatial information. The development needs of these systems provide a rationale as to why—despite an extensive body of research in spatial cognition and spatial linguistics—it is still necessary to investigate basic understandings of how humans conceptualize and communicate about objects and structures in indoor space. This thesis investigates the alignment of conceptual spatial relations and naturallanguage (NL) semantics in the representation of indoor space. The foundation of this work is grounded in spatial information theory as well as spatial cognition and spatial linguistics. In order to better understand how to align computational models and NL expressions about indoor space, this dissertation used an existing dataset of indoor scene descriptions to investigate patterns in entity identification, spatial relations, and spatial preposition use within vista-scale indoor settings. Three human-subject experiments were designed and conducted within virtual indoor environments. These experiments investigate alignment of human-subject NL expressions for a sub-set of conceptual spatial relations (contact, disjoint, and partof) within a controlled virtual environment. Each scene was designed to focus participant attention on a single relation depicted in the scene and elicit a spatial preposition term(s) to describe the focal relationship. The major results of this study are the identification of object and structure categories, spatial relationships, and patterns of spatial preposition use in the indoor scene descriptions that were consistent across both open response, closed response and ranking type items. There appeared to be a strong preference for describing scene objects in relation to the structural objects that bound the room depicted in the indoor scenes. Furthermore, for each of the three relations (contact, disjoint, and partof), a small set of spatial prepositions emerged that were strongly preferred by participants at statistically significant levels based on the overall frequency of response, image sorting, and ranking judgments. The use of certain spatial prepositions to describe relations between room structures suggests there may be differences in how indoor vista-scale space is understood in relation to tabletop and geographic scales. Finally, an indoor scene description corpus was developed as a product of this work, which should provide researchers with new human-subject based datasets for training NL algorithms used to generate more accurate and intuitive NL descriptions of indoor scenes