In this paper, we address the problem of incrementally optimizing constraint networks for maximum likelihood map learning. Our approach allows a robot to efficiently compute configurations of the network with small errors while the robot moves through the environment. We apply a variant of stochastic gradient descent and use a tree-based parameterization of the nodes in the network. By integrating adaptive learning rates in the parameterization of the network, our algorithm can use previously computed solutions to determine the result of the next optimization run. Additionally, our approach updates only the parts of the network which are affected by the newly incorporated measurements and starts the optimization approach only if the new data reveals inconsistencies with the network constructed so far. These improvements yield an efficient solution for this class of online optimization problems. Our approach has been implemented and tested on simulated and on real data. We present comparisons to recently proposed online and offline methods that address the problem of optimizing constraint network. Experiments illustrate that our approach converges faster to a network configuration with small errors than the previous approaches. ©2008 IEEE.

Online constraint network optimization for efficient maximum likelihood map learning / Grisetti, Giorgio; D., Lodi Rizzini; C., Stachniss; E., Olson; W., Burgard. - (2008), pp. 1880-1885. (Intervento presentato al convegno 2008 IEEE International Conference on Robotics and Automation, ICRA 2008 tenutosi a Pasadena, CA nel 19 May 2008 through 23 May 2008) [10.1109/robot.2008.4543481].

Online constraint network optimization for efficient maximum likelihood map learning

GRISETTI, GIORGIO;
2008

Abstract

In this paper, we address the problem of incrementally optimizing constraint networks for maximum likelihood map learning. Our approach allows a robot to efficiently compute configurations of the network with small errors while the robot moves through the environment. We apply a variant of stochastic gradient descent and use a tree-based parameterization of the nodes in the network. By integrating adaptive learning rates in the parameterization of the network, our algorithm can use previously computed solutions to determine the result of the next optimization run. Additionally, our approach updates only the parts of the network which are affected by the newly incorporated measurements and starts the optimization approach only if the new data reveals inconsistencies with the network constructed so far. These improvements yield an efficient solution for this class of online optimization problems. Our approach has been implemented and tested on simulated and on real data. We present comparisons to recently proposed online and offline methods that address the problem of optimizing constraint network. Experiments illustrate that our approach converges faster to a network configuration with small errors than the previous approaches. ©2008 IEEE.
2008
2008 IEEE International Conference on Robotics and Automation, ICRA 2008
04 Pubblicazione in atti di convegno::04c Atto di convegno in rivista
Online constraint network optimization for efficient maximum likelihood map learning / Grisetti, Giorgio; D., Lodi Rizzini; C., Stachniss; E., Olson; W., Burgard. - (2008), pp. 1880-1885. (Intervento presentato al convegno 2008 IEEE International Conference on Robotics and Automation, ICRA 2008 tenutosi a Pasadena, CA nel 19 May 2008 through 23 May 2008) [10.1109/robot.2008.4543481].
File allegati a questo prodotto
File Dimensione Formato  
VE_2008_11573-218599.pdf

solo gestori archivio

Tipologia: Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 446.18 kB
Formato Adobe PDF
446.18 kB Adobe PDF   Contatta l'autore

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/218599
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 54
  • ???jsp.display-item.citation.isi??? 28
social impact