Hippocampal models for simultaneous localisation and mapping on an autonomous robot

To navigate successfully in a novel environment a robot needs to be able to Simultaneously Localize And Map (SLAM) its surroundings. The most successful solutions to this problem so far have involved probabilistic algorithms, but there has been much promising work involving systems based on the workings of part of the rodent brain known as the hippocampus. In this paper we present a biologically plausible system called RatSLAM that uses competitive attractor networks to carry out SLAM in a probabilistic manner. The system can effectively perform parameter self-calibration and SLAM in onedimension. Tests in two dimensional environments revealed the inability of the RatSLAM system to maintain multiple pose hypotheses in the face of ambiguous visual input. These results support recent rat experimentation that suggest current competitive attractor models are not a complete solution to the hippocampal modelling problem.

Probabilistic visual recognition of artificial landmarks for simultaneous localization and mapping

Probabilistic robotics most often applied to the problem of simultaneous localisation and mapping (SLAM), requires measures of uncertainty to accompany observations of the environment. This paper describes how uncertainty can be characterised for a vision system that locates coloured landmarks in a typical laboratory environment. The paper describes a model of the uncertainty in segmentation, the internal cameral model and the mounting of the camera on the robot. It explains the implementation of the system on a laboratory robot, and provides experimental results that show the coherence of the uncertainty model.

The Desert of the Real in Spec Ops: The Line. A Study of Simultaneous Hyperreal Experiences by Protagonist and Player : A Study of Simultaneous Hyperreal Experiences by Protagonist and Player

As an anomaly on the market of military shooters of the 21st century, Spec Ops: The Line entails a journey of undetermined realities and modern warfare consequences. In this study, the narrative is analyzed from the perspective of Jean Baudrillard’s idea that simulations have replaced our conception of reality. Both the protagonist and the player of Spec Ops will unavoidably descend into a state of the hyperreal. They experience multiple possible realities within the game narrative and end up unable to comprehend what has transpired. The hyperreal is defined as the state in which it is impossible to discern reality from simulation. The simulation of reality has proliferated itself into being the reality, and the original has been lost. The excessive use of violence, direct approach of the player through a break with the 4th wall and a deceitful narrator contribute to this loss of reality within the game. Although the game represents simulacra, being a simulation in itself, the object of study is the coexisting state of hyperreal shared between protagonist and player when comprehending events in the game. In the end, neither part can understand or discern with any certainty what transpired within the game.

Data visualization with simultaneous feature selection

Data visualization algorithms and feature selection techniques are both widely used in bioinformatics but as distinct analytical approaches. Until now there has been no method of measuring feature saliency while training a data visualization model. We derive a generative topographic mapping (GTM) based data visualization approach which estimates feature saliency simultaneously with the training of the visualization model. The approach not only provides a better projection by modeling irrelevant features with a separate noise model but also gives feature saliency values which help the user to assess the significance of each feature. We compare the quality of projection obtained using the new approach with the projections from traditional GTM and self-organizing maps (SOM) algorithms. The results obtained on a synthetic and a real-life chemoinformatics dataset demonstrate that the proposed approach successfully identifies feature significance and provides coherent (compact) projections. © 2006 IEEE.