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Autonomous Mapping and Navigation Through Utilization of Edge-Based Optical Flow and Time-to-Collision

  • Madhu Krishnan
  • Mike Wu
  • Young H. KangEmail author
  • Sarah Lee
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 313)

Abstract

This paper proposes a cost-effective approach to map and navigate an area with only the means of a single, low-resolution camera on a “smart robot,” avoiding the cost and unreliability of radar/sonar systems. Implementation is divided into three main parts: object detection, autonomous movement, and mapping by spiraling inwards and using A* Pathfinding algorithm. Object detection is obtained by editing Horn–Schunck’s optical flow algorithm to track pixel brightness factors to subsequent frames, producing outward vectors. These vectors are then focused on the objects using Sobel edge detection. Autonomous movement is achieved by finding the focus of expansion from those vectors and calculating time to collisions, which are then used to maneuver. Algorithms are programmed in MATLAB and JAVA, and implemented with LEGO Mindstorm NXT 2.0 robot for real-time testing with a low-resolution video camera. Through numerous trials and diversity of the situations, validity of results is ensured to autonomously navigate and map a room using solely optical inputs.

Keywords

Autonomous Mapping and Navigation Smart Robot Horn–Schunck’s optical flow algorithm Sobel edge detection A* Pathfinding algorithm 

References

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    Guido Zunino, Simultaneous Localization and Mapping for Navigation in Realistic Environments, Licentiate Thesis, Royal Institute of Technology Numerical Analysis and Computer Science, 2002Google Scholar
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    Guido Zunino, Simultaneous Localization and Mapping for Navigation in Realistic Environments, Licentiate Thesis, Royal Institute of Technology Numerical Analysis and Computer Science, 2002Google Scholar
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    Erico Guizzo, “How Google's Self-Driving Car Works,” IEEE SpectrumGoogle Scholar
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    Pawan Kumar, Len Bottaci, Quasim Mehdi, Norman Gough, and Stephane Natkin, “EFFICIENT PATH FINDING FOR 2D GAMES,” Proceedings of CGAIDE 2004, 2004Google Scholar
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    Horn, Berthold K.P., and Brian G. Schunck. "Determining Optical Flow." Artificial Intelligence, MIT: 185-203. Web. 20 Jan. 2011Google Scholar
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    Amaury Negre, Christophe Braillon, James L. Crowley, and Christian Laugier, “Real time Time To Collision from variation of Intrinsic Scale,” INRIA, Grenoble, FranceGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Madhu Krishnan
    • 1
  • Mike Wu
    • 2
  • Young H. Kang
    • 3
    Email author
  • Sarah Lee
    • 3
  1. 1.University of CaliforniaSan DiegoUSA
  2. 2.Yale UniversityNew HavenUSA
  3. 3.Torrey Pines High SchoolSan DiegoUSA

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