Abstract

We present a solution to the problem of simultaneous localization and mapping (SLAM) based on Dempster-Shafer theory. While several works on the mapping problem based on belief functions exist, none of these approaches deal with the full SLAM problem. In this paper, we derive an evidential version of the FastSLAM algorithm based on a Rao-blackwellized particle filter where belief functions are used for representing a grid map of the robot's environment. The resulting algorithm includes the probabilistic FastSLAM solution as a special case while preserving its low computational complexity. Due to the additional dimensions of uncertainty provided by belief functions, we obtain maps that explicitly show missing information and conflicting sensor measurements. We evaluate our approach using simulation and a real robot equipped with sonar sensors by comparing maps generated by different combination rules, including Bayesian updating.

Abstract

Virtual reality systems provide a convenient means of studying human cognition and performance on a wide range of tasks for which real-world testing is cost prohibitive or difficult to control. For the results of such studies to be valid, it is important to ensure that aspects of the virtual experience do not alter a participant’s behavior or performance on the experimental tasks. This can be particularly difficult when using novel locomotion interfaces that require training. Training procedures should not be completed until movement tasks can be performed at a high level of ability and they do not interfere with concurrent cognitive tasks. A study is described in which subjects were trained to locomote in the Virtusphere, an interface resembling a “human-sized hamster ball.” The effectiveness of training is discussed in terms of both movement abilities and performance on a concurrent cognitive task. Movement performance was tracked as subjects learned to travel through a virtual environment. Additionally, subjects simultaneously completed cognitive tasks in a dual-task selective-interference paradigm. Results showed very rapid improvement on movement measures, including distance traveled and the ratio of collisions to distance traveled, with performance improvement becoming gradual within a few minutes. However, results also highlight persistent problems with concurrent spatial memory tasks, indicating that the training is not really done when performance on the movement metrics levels off.

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Abstract

The evaluation of printing machines poses the problem of how distortions like streaks caused by the machine can be detected and assessed automatically. Although luminance variations in prints can be measured quite precisely, the measured functions bear little relevance for the lightness of streaks and other distortions of prints as perceived by human observers. First, the measurements sometimes indicate changes of luminance in regions which are perceived as homogeneous by humans. Second, the measured strength of a distortion correlates often weakly with its perceived strength, which is influenced by a variety of factors, like the shape of a streak's luminance profile and the distribution of luminance variations in its spatial surround. We have used a model of human perception, based on fundamental neurophysiological and psychophysical properties of the visual system, in order to predict the strength of streak distortions as perceived by human observers from a measured luminance signal. For the evaluation of the model, tests with naive and expert observers have been conducted. They showed that the model has a good correlation (>0.8) to the assessments of human observers and is therefore suited for use in an automatic evaluation system.

Abstract

What are the ingredients required for vision-based place recognition? Pattern recognition models for localization must fulfill invariance requirements different from those of object recognition. We propose a method to evaluate the suitability of existing image processing techniques by testing their outputs against these invariances. The method is applied to several holistic and one local model. We generalize our findings and identify model properties of locality, spatial configuration and generalization as key factors for applicability to localization tasks.

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Abstract

We explore the potential of model identification adaptive controllers (MIAC) within a biofeedback system. Through the application of an adaptive control algorithm, the system performance could be optimised with respect to time. In a series of experiments the galvanic skin resistance of test subjects playing a computer game was recorded. On this data, a system identification was performed, utilising an Adaptive-Network-based Fuzzy Inference System (ANFIS). The results serve as a basis for the development of the adaption laws of the MIAC and allow conclusions about suitable controllers

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Abstract

Perception in natural environments is inseparably linked to motor action. In fact, we consider action an essential component of perceptual representation. But these representations are inherently difficult to investigate: Traditional experimental setups are limited by the lack of flexibility in manipulating spatial features. To overcome these problems, virtual reality (VR) experiments seem to be a feasible alternative, but these setups typically lack ecological realism due to the use of "unnatural" interface-devices (joystick). Thus, we propose an experimental apparatus which combines multisensory perception and action in an ecologically realistic way. The basis is a 10-foot hollow sphere (VirtuSphere) placed on a platform that allows free rotation. A subject inside can walk in any direction for any distance immersed into virtual environment. Both the rotation of the sphere and movement of the subject's head are tracked to process the subject's view within the VR-environment presented on a head-mounted display. Moreover, auditory features are dynamically processed taking greatest care of exact alignment of sound-sources and visual objects using ambisonic-encoded audio processed by a HRTF-filterbank. We present empirical data that confirm ecological realism of this setup and discuss its suitability for multi-sensory-motor research.

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Abstract

One of the reasons why humans are so successful at interpreting everyday situations is that they are able to combine disparate forms of knowledge. Most artificial systems, by contrast, are restricted to a single representation and hence fail to utilize the complementary nature of multiple sources of information. In this paper, we introduce an information-driven scene categorization system that integrates common sense knowledge provided by a domain ontology with a learned statistical model in order to infer a scene class from recognized objects. We show how the unspecificity of coarse logical constraints and the uncertainty of statistical relations and the object detection process can be modeled using Dempster-Shafer theory and derive the resulting belief update equations. In addition, we define an uncertainty minimization principle for adaptively selecting the most informative object detectors and present classification results for scenes from the LabelMe image database.

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Abstract

Apparent motion (AM) is the illusion of a single moving stimulus when discrete stimuli are presented in rapid succession. In previous investigations we found that visual stimuli influence auditory AM perception. In the present investigation we tested the complete range auditory-visual interactions in AM perception by investigating both the influence of visual stimuli on auditory AM and of auditory stimuli on visual AM perception. The experimental setup consisted of stationary sound-light sources, on which AM sequences were presented. The participants had to perform a standard classification task after each stimulus sequence. We found both a facilitation of auditory AM perception by complementing the auditory sequence with visual stimuli and a facilitation of visual AM perception by complementing the visual sequence with auditory stimuli: if stimuli of one modality are added to fill the spatial and temporal gaps of the other modality’s trajectory, we find an increase of the range of SOAs at which continuous motion is perceived. We discuss different models of neural organization. In contrast to the often observed dominance of one sensory system over the other, the effect occurs symmetrically. This supports the notion of an amodal AM interpolation subsystem, which processes visual and auditory stimuli on par.

Abstract

Determining one's position within the environment is a basic feature of spatial behavior and spatial cognition. This task is of inherently sensorimotor nature in that it results from a combination of sensory features and motor actions, where the latter comprise exploratory movements to different positions in the environment. Biological agents achieve this in a robust and effortless fashion, which prompted us to investigate a bio-inspired architecture to study the localization process of an artificial agent which operates in virtual spatial environments. The spatial representation in this architecture is based on sensorimotor features that comprise sensory sensory features as well as motor actions. It is hierarchically organized and its structure can be learned in an unsupervised fashion by an appropriate clustering rule. In addition, the architecture has a temporal belief update mechanism which explicitly utilizes the statistical correlations of actions and locations. The architecture is hybrid in integrating bottom-up processing of sensorimotor features with top-down reasoning which is able to select optimal motor actions based on the principle of maximum information gain. The architecture operates on two sensorimotor levels, a macro-level, which controls the movements of the agent in space, and on a micro-level, which controls its eye movements. As a result, the virtual mobile agent is able to localize itself within an environment using a minimum number of exploratory actions.

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Abstract

We introduce an information-driven scene classification system that combines different types of knowledge derived from a domain ontology and a statistical model in order to analyze scenes based on recognized objects. The domain ontology structures and formalizes which kind of scene classes exist and which object classes occur in them. Based on this structure, an empirical analysis of annotations from the LabelMe image database results in a statistical domain description. Both forms of knowledge are utilized for determining which object class detector to apply to the current scene according to the principle of maximum information gain. All evidence is combined in a belief-based framework that explicitly takes into account the uncertainty inherent to the statistical model and the object detection process as well as the ignorance associated with the coarse granularity of ontological constraints. Finally, we present preliminary classification performance results for scenes from the LabelMe database.

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Abstract

Humans utilize region-based hierarchical representations in the context of navigation. We propose a computational model for representing region hierarchies and define criteria for automatically generating them. We devise a cognitively plausible wayfinding algorithm exploiting the hierarchical decomposition given by regions. The algorithm allows to derive plans with decreasing detail level along paths, enabling an agent to obtain the next action in logarithmic time and complete solutions in almost linear time. The resulting paths are reasonable approximations of optimal shortest paths.

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Abstract

We investigate the hypothesis that the basic representation of space which underlies human navigation does not resemble an image-like map and is not restricted by the laws of Euclidean geometry. For this we developed a new experimental technique in which we use the properties of a virtual environment (VE) to directly influence the development of the representation. We compared the navigation performance of human observers under two conditions. Either the VE is consistent with the geometrical properties of physical space and could hence be represented in a map-like fashion, or it contains severe violations of Euclidean metric and planar topology, and would thus pose difficulties for the correct development of such a representation. Performance is not influenced by this difference, suggesting that a map-like representation is not the major basis of human navigation. Rather, the results are consistent with a representation which is similar to a non-planar graph augmented with path length information, or with a sensorimotor representation which combines sensory properties and motor actions. The latter may be seen as part of a revised view of perceptual processes due to recent results in psychology and neurobiology, which indicate that the traditional strict separation of sensory and motor systems is no longer tenable.

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Abstract

The perception of an image by a human observer is usually modeled as a parallel process in which all parts of the image are treated more or less equivalently, but in reality the analysis of scenes is a highly selective procedure, in which only a small subset of image locations is processed by the precise and efficient neural machinery of foveal vision. To understand the principles behind this selection of the "informative" regions of images, we have developed a hybrid system that consists of a combination of a knowledge-based reasoning system with a low-level preprocessing by linear and nonlinear neural operators. This hybrid system is intended as a first step towards a complete model of the sensorimotor system of saccadic scene analysis. In the analysis of a scene, the system calculates in each step which eye movement has to be made to reach a maximum of information about the scene. The possible information gain is calculated by means of a parallel strategy which is suitable for adaptive reasoning. The output of the system is a fixation sequence, and finally, a hypothesis about the scene.

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