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Vision-Based Reacquisition for Task-Level Control

  • Matthew R. WalterEmail author
  • Yuli Friedman
  • Matthew Antone
  • Seth Teller
Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 79)

Abstract

We describe a vision-based algorithm that enables a robot to “reacquire” objects previously indicated by a human user through simple image-based stylus gestures. By automatically generating a multiple-view appearance model for each object, the method can reacquire the object and reconstitute the user’s segmentation hints even after the robot has moved long distances or significant time has elapsed since the gesture. We demonstrate that this capability enables novel command and control mechanisms: after a human gives the robot a “guided tour” of named objects and their locations in the environment, he can dispatch the robot to fetch any particular object simply by stating its name. We implement the object reacquisition algorithm on an outdoor mobile manipulation platform and evaluate its performance under challenging conditions that include lighting and viewpoint variation, clutter, and object relocation.

Keywords

Ground Truth Appearance Model Sift Feature Multiple Instance Learning Viewpoint Variation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2014

Authors and Affiliations

  • Matthew R. Walter
    • 1
    Email author
  • Yuli Friedman
    • 2
  • Matthew Antone
    • 2
  • Seth Teller
    • 1
  1. 1.MIT CS & AI Lab (CSAIL)CambridgeUSA
  2. 2.BAE SystemsBurlingtonUSA

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