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BatSLAM: Simultaneous Localization and Mapping Using Biomimetic Sonar

Overview of attention for article published in PLOS ONE, January 2013
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Title
BatSLAM: Simultaneous Localization and Mapping Using Biomimetic Sonar
Published in
PLOS ONE, January 2013
DOI 10.1371/journal.pone.0054076
Pubmed ID
Authors

Jan Steckel, Herbert Peremans

Abstract

We propose to combine a biomimetic navigation model which solves a simultaneous localization and mapping task with a biomimetic sonar mounted on a mobile robot to address two related questions. First, can robotic sonar sensing lead to intelligent interactions with complex environments? Second, can we model sonar based spatial orientation and the construction of spatial maps by bats? To address these questions we adapt the mapping module of RatSLAM, a previously published navigation system based on computational models of the rodent hippocampus. We analyze the performance of the proposed robotic implementation operating in the real world. We conclude that the biomimetic navigation model operating on the information from the biomimetic sonar allows an autonomous agent to map unmodified (office) environments efficiently and consistently. Furthermore, these results also show that successful navigation does not require the readings of the biomimetic sonar to be interpreted in terms of individual objects/landmarks in the environment. We argue that the system has applications in robotics as well as in the field of biology as a simple, first order, model for sonar based spatial orientation and map building.

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Geographical breakdown

Country Count As %
Colombia 1 <1%
India 1 <1%
Belgium 1 <1%
Unknown 121 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 30 24%
Student > Master 23 19%
Researcher 10 8%
Student > Bachelor 7 6%
Student > Doctoral Student 5 4%
Other 15 12%
Unknown 34 27%
Readers by discipline Count As %
Engineering 51 41%
Computer Science 18 15%
Agricultural and Biological Sciences 11 9%
Neuroscience 4 3%
Environmental Science 1 <1%
Other 6 5%
Unknown 33 27%