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

  • Strasdat H
  • Stachniss C
  • Burgard W
  • 42


    Mendeley users who have this article in their library.
  • N/A


    Citations of this article.


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 furthermore demonstrate that the learned policies are not only usable in a specific scenario but can also be generalized towards environments with varying properties.

Get free article suggestions today

Mendeley saves you time finding and organizing research

Sign up here
Already have an account ?Sign in

Find this document


  • Hauke Strasdat

  • Cyrill Stachniss

  • Wolfram Burgard

Cite this document

Choose a citation style from the tabs below

Save time finding and organizing research with Mendeley

Sign up for free