DocumentCode :
3521757
Title :
Brain-inspired sensor fusion for navigating robots
Author :
Milford, Michael J. ; Jacobson, Alec
Author_Institution :
Sch. of Electr. Eng. & Comput. Sci., Queensland Univ. of Technol., Brisbane, QLD, Australia
fYear :
2013
fDate :
6-10 May 2013
Firstpage :
2906
Lastpage :
2913
Abstract :
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.
Keywords :
SLAM (robots); mobile robots; path planning; robot vision; sensor fusion; Pioneer robot; brain-inspired sensor fusion; dynamic weighting; environmental conditions; indoor environments; navigating robots; outdoor environments; place mapping; place recognition; rat-inspired featureless sensor-fusion system; robot mapping system; robot navigation system; Cameras; Coherence; Rats; Robot sensing systems; Sensor fusion; Sonar;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Robotics and Automation (ICRA), 2013 IEEE International Conference on
Conference_Location :
Karlsruhe
ISSN :
1050-4729
Print_ISBN :
978-1-4673-5641-1
Type :
conf
DOI :
10.1109/ICRA.2013.6630980
Filename :
6630980
Link To Document :
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