English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
 
 
DownloadE-Mail
  Learning view graphs for robot navigation

Franz, M., Schölkopf, B., Georg, P., Mallot, H., & Bülthoff, H. (1997). Learning view graphs for robot navigation. In First International Conference on Autonomous Agents (AGENTS '97) (pp. 138-147). New York, NY, USA: ACM Press.

Item is

Files

show Files

Locators

show

Creators

show
hide
 Creators:
Franz, MO1, Author           
Schölkopf, B1, Author           
Georg, P2, Author           
Mallot, HA2, Author           
Bülthoff, HH2, Author           
Johnson, W.L., Editor
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497797              

Content

show
hide
Free keywords: -
 Abstract: We present a purely vision-based scheme for learning a parsimonious representation of an open environment. Using simple exploration behaviours, our system constructs a graph of appropriately chosen views. To navigate between views connected in the graph, we employ a homing strategy inspired by findings of insect ethology. Simulations and robot experiments demonstrate the feasibility of the proposed approach.

Details

show
hide
Language(s):
 Dates: 1997-02
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: ISBN: 0-89791-877-0
URI: http://portal.acm.org/citation.cfm?id=267687
DOI: 10.1145/267658.267687
BibTex Citekey: 358
 Degree: -

Event

show
hide
Title: First International Conference on Autonomous Agents (AGENTS '97)
Place of Event: Marina del Rey, CA, USA
Start-/End Date: -

Legal Case

show

Project information

show

Source 1

show
hide
Title: First International Conference on Autonomous Agents (AGENTS '97)
Source Genre: Proceedings
 Creator(s):
Affiliations:
Publ. Info: New York, NY, USA : ACM Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 138 - 147 Identifier: -