Learning View Graphs for Robot Navigation

Matthias O. Franz, Bernhard Schölkopf, Hanspeter A. Mallot, Heinrich H. Bülthoff

Research output: Contribution to journalArticlepeer-review

100 Citations (Scopus)


We present a purely vision-based scheme for learning a topological representation of an open environment. The system represents selected places by local views of the surrounding scene, and finds traversable paths between them. The set of recorded views and their connections are combined into a graph model of the environment. To navigate between views connected in the graph, we employ a homing strategy inspired by findings of insect ethology. In robot experiments, we demonstrate that complex visual exploration and navigation tasks can thus be performed without using metric information.

Original languageEnglish
Pages (from-to)111-125
Number of pages15
JournalAutonomous Robots
Issue number1
Publication statusPublished - 1998


  • Cognitive maps
  • Environment modeling
  • Exploration
  • Mobile robots
  • Omnidirectional sensor
  • Topological maps
  • Visual navigation

ASJC Scopus subject areas

  • Artificial Intelligence


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