Development of dog-like retrieving capability in a ground robot

Douglas C. MacKenzie, Rahul Ashok, James M. Rehg, Gary Witus

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper presents the Mobile Intelligence Team's approach to addressing the CANINE outdoor ground robot competition. The competition required developing a robot that provided retrieving capabilities similar to a dog, while operating fully autonomously in unstructured environments. The vision team consisted of Mobile Intelligence, the Georgia Institute of Technology, and Wayne State University. Important computer vision aspects of the project were the ability to quickly learn the distinguishing characteristics of novel objects, searching images for the object as the robot drove a search pattern, identifying people near the robot for safe operations, correctly identify the object among distractors, and localizing the object for retrieval. The classifier used to identify the objects will be discussed, including an analysis of its performance, and an overview of the entire system architecture presented. A discussion of the robot's performance in the competition will demonstrate the system's successes in real-world testing.

Original languageEnglish (US)
Title of host publicationProceedings of SPIE-IS and T Electronic Imaging - Intelligent Robots and Computer Vision XXX
Subtitle of host publicationAlgorithms and Techniques
DOIs
StatePublished - 2013
Externally publishedYes
EventIntelligent Robots and Computer Vision XXX: Algorithms and Techniques - Burlingame, CA, United States
Duration: Feb 4 2013Feb 6 2013

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume8662
ISSN (Print)0277-786X

Conference

ConferenceIntelligent Robots and Computer Vision XXX: Algorithms and Techniques
Country/TerritoryUnited States
CityBurlingame, CA
Period2/4/132/6/13

Keywords

  • autonomy
  • CANINE
  • computer vision
  • object recognition
  • unmanned ground vehicle

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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