Autonomous movement-driven place recognition calibration for generic multi-sensor robot platforms
Jacobson, Adam, Chen, Zetao, & Milford, Michael (2013) Autonomous movement-driven place recognition calibration for generic multi-sensor robot platforms. In Sugano, S. & Kaneko, M. (Eds.) Proceedings of the 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE, Tokyo Big Sight, Tokyo, 1314 -1320.
In this paper we present a method for autonomously tuning the threshold between learning and recognizing a place in the world, based on both how the rodent brain is thought to process and calibrate multisensory data and the pivoting movement behaviour that rodents perform in doing so. The approach makes no assumptions about the number and type of sensors, the robot platform, or the environment, relying only on the ability of a robot to perform two revolutions on the spot. In addition, it self-assesses the quality of the tuning process in order to identify situations in which tuning may have failed. We demonstrate the autonomous movement-driven threshold tuning on a Pioneer 3DX robot in eight locations spread over an office environment and a building car park, and then evaluate the mapping capability of the system on journeys through these environments. The system is able to pick a place recognition threshold that enables successful environment mapping in six of the eight locations while also autonomously flagging the tuning failure in the remaining two locations. We discuss how the method, in combination with parallel work on autonomous weighting of individual sensors, moves the parameter dependent RatSLAM system significantly closer to sensor, platform and environment agnostic operation.
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|Item Type:||Conference Paper|
|Divisions:||Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
|Copyright Owner:||Copyright 2013 IEEE|
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|Deposited On:||21 Jan 2014 00:49|
|Last Modified:||09 Sep 2016 05:20|
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