Physically Based Simulation Model for Acoustic Sensor Robot Navigation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Qualitative reasoning: modeling and simulation with incomplete knowledge
Qualitative reasoning: modeling and simulation with incomplete knowledge
Building a Local Hybrid Map from Sensor Data Fusion
Proceedings of the 2006 conference on Artificial Intelligence Research and Development
Qualitative distances and qualitative image descriptions for representing indoor scenes in robotics
Pattern Recognition Letters
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Sensor based navigation is fundamental to any mobile robot. Conventional statistical approaches to the navigation problem maintain an exact global description of environment geometry. However, in practise, the behaviour of real physical sensors and the observations they make of the environment make such central geometric representations extremely fragile. To overcome such problems, this paper proposes the use of qualitative models of physical sensor observations. These aim to describe the world in terms of local sensor-centric representations of the observed environment. Each representation exploits those landmarks most natural to the physical sensor involved and no explicit geometric representation of the world is assumed. This leads naturally to a navigation process defined in terms of relationships between different sensor observables; an intrinsically more robust mechanism than found in conventional navigation algorithms. The representation and navigation methodology proposed is illustrated using sonar data from a real vehicle.