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Learning view graphs for robot navigation




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.

Author(s): Franz, M. and Schölkopf, B. and Mallot, HA. and Bülthoff, HH. and
Journal: Autonomous Robots
Volume: 5
Number (issue): 1
Pages: 111-125
Year: 1998
Month: March
Day: 0

Department(s): Empirical Inference
Bibtex Type: Article (article)

Digital: 0
DOI: 10.1023/A:1008821210922
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF


  title = {Learning view graphs for robot navigation},
  author = {Franz, M. and Sch{\"o}lkopf, B. and Mallot, HA. and B{\"u}lthoff, HH. and},
  journal = {Autonomous Robots},
  volume = {5},
  number = {1},
  pages = {111-125},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = mar,
  year = {1998},
  month_numeric = {3}