Acquiring models of the environment belongs to the fundamental tasks of mobile robots. Approaches addressing the problem of simultaneous localization and mapping (SLAM) typically process the perceived sensor data and do not influence the motion of the mobile robot In this paper, we present an approach to actively closing loops during exploration. It applies a Rao-Blackwellized particle filter to maintain multiple hypotheses about potential trajectories of the robot and corresponding maps. To prevent the particle filter from becoming overly confident, we present a technique to recover the particle diversity after successfully closing a loop. This way the particle depletion problem is avoided. The combination of our approach with the active loop closing strategy allows to deal with multiple nested loops. Experimental results presented in this paper illustrate the advantage of our method over pervious approaches to mapping with Rao-Blackwellized particle filters. © 2005 IEEE.

Recovering particle diversity in a Rao-Blackwellized particle filter for SLAM after actively closing loops / C., Stachniss; Grisetti, Giorgio; W., Burgard. - 2005:(2005), pp. 655-660. (Intervento presentato al convegno 2005 IEEE International Conference on Robotics and Automation tenutosi a Barcelona) [10.1109/robot.2005.1570192].

Recovering particle diversity in a Rao-Blackwellized particle filter for SLAM after actively closing loops

GRISETTI, GIORGIO;
2005

Abstract

Acquiring models of the environment belongs to the fundamental tasks of mobile robots. Approaches addressing the problem of simultaneous localization and mapping (SLAM) typically process the perceived sensor data and do not influence the motion of the mobile robot In this paper, we present an approach to actively closing loops during exploration. It applies a Rao-Blackwellized particle filter to maintain multiple hypotheses about potential trajectories of the robot and corresponding maps. To prevent the particle filter from becoming overly confident, we present a technique to recover the particle diversity after successfully closing a loop. This way the particle depletion problem is avoided. The combination of our approach with the active loop closing strategy allows to deal with multiple nested loops. Experimental results presented in this paper illustrate the advantage of our method over pervious approaches to mapping with Rao-Blackwellized particle filters. © 2005 IEEE.
2005
2005 IEEE International Conference on Robotics and Automation
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
Recovering particle diversity in a Rao-Blackwellized particle filter for SLAM after actively closing loops / C., Stachniss; Grisetti, Giorgio; W., Burgard. - 2005:(2005), pp. 655-660. (Intervento presentato al convegno 2005 IEEE International Conference on Robotics and Automation tenutosi a Barcelona) [10.1109/robot.2005.1570192].
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