We investigate the relation between the physical world and its mental representation in the 'cognitive map', and test if this representation is image-like and complies with the laws of Euclidean geometry. We have developed a new experimental technique using 'impossible' virtual environments (VE) to directly influence the representational development. Subjects explore a number of VEs -- some 'normal', others with severe violations of Euclidean metrics or planar topology. We check if these manipulated properties cause problems in navigation performance. A consistent VE should be easily represented mentally in a map-like fashion, while a VE with severe violations should prove difficult. Surprisingly, we found no substantial influence of the impossible VEs on navigation performance, and forced-choice tests showed little evidence that subjects were aware of manipulations. This suggests that the representation does not resemble a two-dimensional image-like map. Alternatives to consider are sensorimotor and graph-like representations.

We investigate a hybrid system for autonomous exploration and navigation, and implement it in a virtual mobile agent, which operates in virtual spatial environments. The system is based on several distinguishing properties. The representation is not map-like, but based on sensorimotor features, i.e. on combinations of sensory features and motor actions. The system has a hybrid architecture, which integrates a bottom-up processing of sensorimotor features with a top-down, knowledge-based reasoning strategy. This strategy selects the optimal motor action in each step according to the principle of maximum information gain. Two sensorimotor levels with different behavioural granularity are implemented, a macro-level, which controls the movements of the agent in space, and a micro-level, which controls its eye movements. At each level, the same type of hybrid architecture and the same principle of information gain are used for sensorimotor control. The localisation performance of the system is tested with large sets of virtual rooms containing different mixtures of unique and non-unique objects. The results demonstrate that the system efficiently performs those exploratory motor actions that yield a maximum amount of information about the current environment. Localisation is typically achieved within a few steps. Furthermore, the computational complexity of the underlying computations is limited, and the system is robust with respect to minor variations in the spatial environments.

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.

Place recognitionThe concept of place is essential to the way humans represent and interact with spatial environments. This raises the question of how ''being at a place'' can be inferred from sensory information. The investigation of place cells, for example, indicates the importance of visual cues for the robust localization of rodents (O'Keefe and Dostrovsky 1971), however, the exact processing mechanisms remain unclear. The activation of a place cell is primarily determined by the animal's location. Typically, it is independent of the orientation and other conditions like illumination. This kind of independence from certain aspects of the sensory input is a key challenge in the field of pattern recognition where it is referred to as invariance. A prominent example is recognizing objects invariantly under transformations resulting from changes in the perspective on the object (for example recognizing the tree in both Fig. 1a and d). In this paper, our goal is to investigate the invariance properties specific to place recognition in order to draw conclusions about the suitability of different image processing techniques.The variance in the visual input perceived at a specific place mainly results from minor changes of the observer's orientation whereas the variance between places results from changes in position. Strictly speaking, any change in position leads to another place, but, for most purposes, the granularity of a place as a local environment, like in place cells, is desirable. The question thus is: What are the consequences of the different changes for the projection of the environment on the retina? (see Fig. 1) Changes in position orthogonal to the viewing direction (e.g., taking a step to the left) roughly correspond to a translation of the projected pattern. The extent of the translation depends on the depth structure of the perceived scene (motion parallax) which can lead to occlusions and distortions. Changes in position along the viewing direction lead to changes in scale and similar occlusions/distortions. Changes in the observer's orientation relate to a translation of the pattern on the retina including minor distortions, depending on the lens and deviations with respect to the rotation axis from the nodal point. Overall, place recognition should be invariant under minor translations of the perceived pattern, but it should be selective with respect to major changes in scale or occlusions resulting from translation by the observer. By contrast, object recognition approaches are typically designed for achieving invariance under changes in scale, partial occlusions and out-of-plane rotations.Having specified the invariance requirements for place recognition, we now try to relate the possible approaches to place recognition with the broader realm of pattern recognition. For this, we suggest a conceptual space with three basic dimensions.In the first dimension, we distinguish between local and global (holistic) approaches. A local representation relies on information extracted at single points or ...

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