Proceedings ArticleDOI
Sensor resetting localization for poorly modelled mobile robots
Scott Lenser,Manuela Veloso +1 more
- Vol. 2, pp 1225-1232
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TLDR
A new localization algorithm, called sensor resetting localization, which is an extension of Monte Carlo localization, is presented, which has been successfully used on autonomous legged robots in the Sony legged league of the robotic soccer competition RoboCup'99.Abstract:
We present a new localization algorithm, called sensor resetting localization, which is an extension of Monte Carlo localization. The algorithm adds sensor based re-sampling to Monte Carlo localization when the robot is lost. Sensor resetting localization (SRL) is robust to modelling errors including unmodelled movements and systematic errors. It can be used in real time on systems with limited computational power. The algorithm has been successfully used on autonomous legged robots in the Sony legged league of the robotic soccer competition RoboCup'99. We present results from the real robots demonstrating the success of the algorithm and results from simulation comparing SRL to Monte Carlo localization.read more
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Sequential Monte Carlo methods in practice
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References
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Proceedings ArticleDOI
Monte Carlo localization for mobile robots
TL;DR: The Monte Carlo localization method is introduced, where the probability density is represented by maintaining a set of samples that are randomly drawn from it, and it is shown that the resulting method is able to efficiently localize a mobile robot without knowledge of its starting location.
Proceedings Article
Monte Carlo localization: efficient position estimation for mobile robots
TL;DR: Monte Carlo Localization is a version of Markov localization, a family of probabilistic approaches that have recently been applied with great practical success and yields improved accuracy while requiring an order of magnitude less computation when compared to previous approaches.
Proceedings Article
Probabilistic robot navigation in partially observable environments
Reid Simmons,Sven Koenig +1 more
TL;DR: First results are reported on first results of a research program that uses par tially observable Markov models to robustly track a robots location in office environments and to direct its goal-oriented actions.
Journal ArticleDOI
Active Markov localization for mobile robots
TL;DR: The approach is based on Markov localization and provides rational criteria for setting the robot’s motion direction (exploration), and determining the pointing direction of the sensors so as to most efficiently localize the robot.
Proceedings Article
Estimating the absolute position of a mobile robot using position probability grids
TL;DR: The position probability grid approach to estimating the robot's absolute position and orientation in a metric model of the environment is described, designed to work with standard sensors and is independent of any knowledge about the starting point.