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Paladyn, Journal of Behavioral Robotics

Editor-in-Chief: Schöner, Gregor

1 Issue per year


CiteScore 2017: 0.33

SCImago Journal Rank (SJR) 2017: 0.104

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2081-4836
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Experience based localization in wide open indoor environments

Rahul Gautam
  • Corresponding author
  • Department of Information Technology, Indian Institute of Information Technology Allahabad, Allahabad, India
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/ Harsh Jain
  • Department of Information Technology, Indian Institute of Information Technology Allahabad, Allahabad, India
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/ Mayank Poply
  • Department of Information Technology, Indian Institute of Information Technology Allahabad, Allahabad, India
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/ Rajkumar Jain
  • Department of Information Technology, Indian Institute of Information Technology Allahabad, Allahabad, India
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/ Mukul Anand
  • Department of Information Technology, Indian Institute of Information Technology Allahabad, Allahabad, India
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/ Rahul Kala
  • Department of Information Technology, Indian Institute of Information Technology Allahabad, Allahabad, India
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Published Online: 2018-05-24 | DOI: https://doi.org/10.1515/pjbr-2018-0006

Abstract

This paper solves the problem of localization for indoor environments using visual place recognition, visual odometry and experience based localization using a camera. Our main motivation is just like a human is able to recall from its past experience, a robot should be able to use its recorded visual memory in order to determine its location. Currently experience based localization has been used in constrained environments like outdoor roads, where the robot is constrained to the same set of locations during every visit. This paper adapts the same technology to wide open maps like halls wherein the robot is not constrained to specific locations. When a robot is turned on in a room, it first uses visual place recognition using histogram of oriented gradients and support vector machine in order to predict which room it is in. It then scans its surroundings and uses a nearest neighbor search of the robot’s experience coupled with visual odometry for localization. We present the results of our approach test on a dynamic environment comprising of three rooms. The dataset consists of approximately 5000 monocular and 5000 depth images.

Keywords: localization; visual place recognition; experience based localization; machine learning

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About the article

Received: 2017-12-17

Accepted: 2018-03-27

Published Online: 2018-05-24


Citation Information: Paladyn, Journal of Behavioral Robotics, Volume 9, Issue 1, Pages 82–94, ISSN (Online) 2081-4836, DOI: https://doi.org/10.1515/pjbr-2018-0006.

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© 2018 Rahul Gautam, published by Sciendo. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. BY-NC-ND 4.0

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