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Persistent navigation and mapping using a biologically inspired SLAM system

Milford, Michael & Wyeth, Gordon (2009) Persistent navigation and mapping using a biologically inspired SLAM system. The International Journal of Robotics Research, pp. 1-23.

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Abstract

The challenge of persistent navigation and mapping is to develop an autonomous robot system that can simultaneously localize, map and navigate over the lifetime of the robot with little or no human intervention. Most solutions to the simultaneous localization and mapping (SLAM) problem aim to produce highly accurate maps of areas that are assumed to be static. In contrast, solutions for persistent navigation and mapping must produce reliable goal-directed navigation outcomes in an environment that is assumed to be in constant flux. We investigate the persistent navigation and mapping problem in the context of an autonomous robot that performs mock deliveries in a working office environment over a two-week period. The solution was based on the biologically inspired visual SLAM system, RatSLAM. RatSLAM performed SLAM continuously while interacting with global and local navigation systems, and a task selection module that selected between exploration, delivery, and recharging modes. The robot performed 1,143 delivery tasks to 11 different locations with only one delivery failure (from which it recovered), traveled a total distance of more than 40 km over 37 hours of active operation, and recharged autonomously a total of 23 times.

Impact and interest:

44 citations in Scopus
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32 citations in Web of Science®

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ID Code: 32815
Item Type: Journal Article
Keywords: persistent navigation and mapping, SLAM, RatSLAM, biologically inspied
DOI: 10.1177/0278364909340592
ISSN: 0278-3649
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Adaptive Agents and Intelligent Robotics (080101)
Deposited On: 23 Jun 2010 10:59
Last Modified: 01 Mar 2012 00:14

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