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Type Conference or Workshop Paper
Scope Discipline-based scholarship
Published in Proceedings Yes
Title "On-the-spot Training" for Terrain Classification in Autonomous Air-Ground Collaborative Teams
Organization Unit
Authors
  • Jeffrey Delmerico
  • Alessandro Giusti
  • Elias Müggler
  • Luca Gambardella
  • Davide Scaramuzza
Presentation Type paper
Item Subtype Original Work
Refereed Yes
Status Published in final form
Language
  • English
Event Title International Symposium on Experimental Robotics
Event Type conference
Event Location Tokyo, Japan
Event Start Date October 3 - 2016
Event End Date October 6 - 2016
Place of Publication New York
Publisher Springer
Abstract Text We consider the problem of performing rapid training of a terrain classifier in the context of a collaborative robotic search and rescue system. Our system uses a vision-based flying robot to guide a ground robot through unknown terrain to a goal location by building a map of terrain class and elevation. However, due to the unknown environments present in search and rescue scenarios, our system requires a terrain classifier that can be trained and deployed quickly, based on data collected on the spot. We investigate the relationship of training set size and complexity on training time and accuracy, for both feature-based and convolutional neural network classifiers in this scenario. Our goal is to minimize the deployment time of the classifier in our terrain mapping system within acceptable classification accuracy tolerances. So we are not concerned with training a classifier that generalizes well, only one that works well for this particular environment. We demonstrate that we can launch our aerial robot, gather data, train a classifier, and begin building a terrain map after only 60 seconds of flight.
Digital Object Identifier 10.1007/978-3-319-50115-4_50
Other Identification Number merlin-id:13665
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