Probabilistic Robotics (Intelligent Robotics and Autonomous Agents)
Probabilistic Robotics (Intelligent Robotics and Autonomous Agents)
The Graph SLAM Algorithm with Applications to Large-Scale Mapping of Urban Structures
International Journal of Robotics Research
Augmenting appearance-based localization and navigation using belief update
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 2 - Volume 2
TAMCRA: a tunable accuracy multiple constraints routing algorithm
Computer Communications
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The problem of robot navigation is a fundamental problem for every mobile robot: How to make a robot travel from point A to point B on a given map with maximal efficiency. Solving the problem of robot navigation can be trivial if the robot has means to determine its position in the world at any time by using, for example, reliable sensors. However, in some cases localization means are nonexistent (for example the use of a GPS in indoor environments) or costly (for example the use of laser sensors). In these cases, the problem of robot navigation becomes far more complicated, even when a map is given. The main objective of this paper is to determine a quantitative measure for determining the possibility of navigating in indoor environments given a map for a robot without perfect localization, and to find a navigation path that maximizes the chances of arriving at the destination point safely.