Position Estimation for Drones based on Visual SLAM and IMU in GPS-denied Environment

Hamid Didari Khamseh Motlagh, Faraz Lotfi, Hamid D. Taghirad, Saeed Bakhshi Germi

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

16 Citations (Scopus)

Abstract

Due to the increased rate of drone usage in various commercial and industrial fields, the need for their autonomous operation is rapidly increasing. One major aspect of autonomous movement is the ability to operate safely in an unknown environment. The majority of current works are persistently using a global positioning system (GPS) to directly find the absolute position of the drone. However, GPS accuracy might be not suitable in some applications and this solution is not applicable to all situations. In this paper, a positioning system based on monocular SLAM and inertial measurement unit (IMU) is presented. The position is calculated through the semi-direct visual odometry (SVO) method alongside IMU data, and is integrated with an extended Kalman filter (EKF) to enhance the efficiency of the algorithm. The data is then employed to control the drone without any requirement to any source of external input. The experiment results for long-distance flying paths is very promising.

Original languageEnglish
Title of host publicationICRoM 2019 - 7th International Conference on Robotics and Mechatronics
PublisherIEEE
Pages120-124
Number of pages5
ISBN (Electronic)9781728166049
DOIs
Publication statusPublished - 1 Nov 2019
Publication typeA4 Article in conference proceedings
EventInternational Conference on Robotics and Mechatronics - Tehran, Iran, Islamic Republic of
Duration: 20 Nov 201921 Nov 2019

Conference

ConferenceInternational Conference on Robotics and Mechatronics
Country/TerritoryIran, Islamic Republic of
CityTehran
Period20/11/1921/11/19

Keywords

  • Kalman filtering
  • monocular camera
  • Position estimation
  • SLAM
  • UAV

Publication forum classification

  • Publication forum level 0

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Mechanical Engineering
  • Control and Optimization
  • Instrumentation

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