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ICRA
2009
IEEE

Which landmark is useful? Learning selection policies for navigation in unknown environments

9 years 6 months ago
Which landmark is useful? Learning selection policies for navigation in unknown environments
Abstract— In general, a mobile robot that operates in unknown environments has to maintain a map and has to determine its own location given the map. This introduces significant computational and memory constraints for most autonomous systems, especially for lightweight robots such as humanoids or flying vehicles. In this paper, we present a novel approach for learning a landmark selection policy that allows a robot to discard landmarks that are not valuable for its current navigation task. This enables the robot to reduce the computational burden and to carry out its task more efficiently by maintaining only the important landmarks. Our approach applies an unscented Kalman filter for addressing the simultaneous localization and mapping problems and uses Monte-Carlo reinforcement learning to obtain the selection policy. Based on real world and simulation experiments, we show that the learned policies allow for efficient robot navigation and outperform handcrafted strategies. We ...
Hauke Strasdat, Cyrill Stachniss, Wolfram Burgard
Added 23 May 2010
Updated 23 May 2010
Type Conference
Year 2009
Where ICRA
Authors Hauke Strasdat, Cyrill Stachniss, Wolfram Burgard
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