Monocular visual mapping for obstacle avoidance on UAVs

Daniel Magree, John G. Mooney, Eric Johnson

Research output: Contribution to journalArticlepeer-review

16 Scopus citations

Abstract

An unmanned aerial vehicle requires adequate knowledge of its surroundings in order to operate in close proximity to obstacles. UAVs also have strict payload and power constraints which limit the number and variety of sensors available to gather this information. It is desirable, therefore, to enable a UAV to gather information about potential obstacles or interesting landmarks using common and lightweight sensor systems. This paper presents a method of fast terrain mapping with a monocular camera. Features are extracted from camera images and used to update a sequential extended Kalman filter. The features locations are parameterized in inverse depth to enable fast depth convergence. Converged features are added to a persistent terrain map which can be used for obstacle avoidance and additional vehicle guidance. Simulation results, results from recorded flight test data, and flight test results are presented to validate the algorithm.

Original languageEnglish (US)
Pages (from-to)17-26
Number of pages10
JournalJournal of Intelligent and Robotic Systems: Theory and Applications
Volume74
Issue number1-2
DOIs
StatePublished - Apr 1 2014

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Mechanical Engineering
  • Industrial and Manufacturing Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering

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