Brain-inspired sensor fusion for navigating robots
Milford, Michael & Jacobson, Adam (2013) Brain-inspired sensor fusion for navigating robots. In Proceedings of the 2013 IEEE International Conference on Robotics and Automation (ICRA), IEEE, Kongresszentrum Karlsruhe, Karlsruhe, Germany, pp. 2906-2913.
Current state of the art robot mapping and navigation systems produce impressive performance under a narrow range of robot platform, sensor and environmental conditions, in contrast to animals such as rats that produce “good enough” maps that enable them to function under an incredible range of situations. In this paper we present a rat-inspired featureless sensor-fusion system that assesses the usefulness of multiple sensor modalities based on their utility and coherence for place recognition, without knowledge as to the type of sensor. We demonstrate the system on a Pioneer robot in indoor and outdoor environments with abrupt lighting changes. Through dynamic weighting of the sensors, the system is able to perform correct place recognition and mapping where the static sensor weighting approach fails.
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|Item Type:||Conference Paper|
|Keywords:||SLAM (robots), Mobile robots, Path planning, Robot vision, Sensor fusion|
|Divisions:||Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
|Copyright Owner:||Copyright 2013 IEEE|
|Deposited On:||22 Jan 2014 02:58|
|Last Modified:||09 Sep 2016 05:20|
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