Obstacle Detection and Classification with Portable Uncalibrated Patterned Projected Light

Maria Cornacchia, Yu Zheng, Burak Kakillioglu, Senem Velipasalar

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

Abstract

Autonomous obstacle avoidance systems are instrumental for applications including assisting visually impaired people, guiding autonomous robots, and assisted driving. In this paper, we propose a novel method for autonomous obstacle detection and recognition, which employs a different type of sensor incorporating a patterned light with camera, and provides a lightweight, cost-conscious, energy-efficient, reliable and portable solution. Experimental results indicate that grids, projected by the patterned light source, are apparent and differentiable as the sensing system is hand-carried in both indoor and outdoor environments over and towards different types of obstacles. We propose methods for exploiting these patterns for obstacle detection and classification without requiring calibration. We demonstrate that obstacles can be classified by using our novel patterned light and camera setup and employing a Convolutional Neural Network (CNN)-based approach. The proposed method provides very promising results with overall detection and classification accuracies of 93.11% and 86.06% respectively.

Original languageEnglish (US)
Title of host publicationConference Record of the 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018
EditorsMichael B. Matthews
PublisherIEEE Computer Society
Pages2230-2234
Number of pages5
ISBN (Electronic)9781538692189
DOIs
StatePublished - Feb 19 2019
Event52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018 - Pacific Grove, United States
Duration: Oct 28 2018Oct 31 2018

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
Volume2018-October
ISSN (Print)1058-6393

Conference

Conference52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018
CountryUnited States
CityPacific Grove
Period10/28/1810/31/18

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Keywords

  • Convolutional Neural Networks
  • Obstacle Classification
  • Obstacle Detection
  • Patterned Light

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

Cite this

Cornacchia, M., Zheng, Y., Kakillioglu, B., & Velipasalar, S. (2019). Obstacle Detection and Classification with Portable Uncalibrated Patterned Projected Light. In M. B. Matthews (Ed.), Conference Record of the 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018 (pp. 2230-2234). [8645346] (Conference Record - Asilomar Conference on Signals, Systems and Computers; Vol. 2018-October). IEEE Computer Society. https://doi.org/10.1109/ACSSC.2018.8645346