Development of a SLAM System for Small UAVs in Indoor Environments using Gaussian Processes

Young San Jeon, Jongeun Choi, Jeong Oog Lee

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

Localization of aerial vehicles and map building of flight environments are key technologies for the autonomous flight of small UAVs. In outdoor environments, an unmanned aircraft can easily use a GPS (Global Positioning System) for its localization with acceptable accuracy. However, as the GPS is not available for use in indoor environments, the development of a SLAM (Simultaneous Localization and Mapping) system that is suitable for small UAVs is therefore needed. In this paper, we suggest a vision-based SLAM system that uses vision sensors and an AHRS (Attitude Heading Reference System) sensor. Feature points in images captured from the vision sensor are obtained by using GPU (Graphics Process Unit) based SIFT (Scale-invariant Feature Transform) algorithm. Those feature points are then combined with attitude information obtained from the AHRS to estimate the position of the small UAV. Based on the location information and color distribution, a Gaussian process model is generated, which could be a map. The experimental results show that the position of a small unmanned aircraft is estimated properly and the map of the environment is constructed by using the proposed method. Finally, the reliability of the proposed method is verified by comparing the difference between the estimated values and the actual values.

Original languageKorean
Pages (from-to)1098-1102
Number of pages5
JournalJournal of Institute of Control, Robotics and Systems
Volume20
Issue number11
DOIs
Publication statusPublished - 2014 Nov 1

Fingerprint

Simultaneous Localization and Mapping
Scale Invariant Feature Transform
Navigation System
Gaussian distribution
Unmanned aerial vehicles (UAV)
Navigation systems
Gaussian Process
Computer Vision
Computer vision
Aircraft
Robotics
Global Positioning System
Global positioning system
Sensors
Feature Point
Sensor
Chemical reactions
Gaussian Model
Mathematical transformations
Antennas

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Applied Mathematics

Cite this

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abstract = "Localization of aerial vehicles and map building of flight environments are key technologies for the autonomous flight of small UAVs. In outdoor environments, an unmanned aircraft can easily use a GPS (Global Positioning System) for its localization with acceptable accuracy. However, as the GPS is not available for use in indoor environments, the development of a SLAM (Simultaneous Localization and Mapping) system that is suitable for small UAVs is therefore needed. In this paper, we suggest a vision-based SLAM system that uses vision sensors and an AHRS (Attitude Heading Reference System) sensor. Feature points in images captured from the vision sensor are obtained by using GPU (Graphics Process Unit) based SIFT (Scale-invariant Feature Transform) algorithm. Those feature points are then combined with attitude information obtained from the AHRS to estimate the position of the small UAV. Based on the location information and color distribution, a Gaussian process model is generated, which could be a map. The experimental results show that the position of a small unmanned aircraft is estimated properly and the map of the environment is constructed by using the proposed method. Finally, the reliability of the proposed method is verified by comparing the difference between the estimated values and the actual values.",
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Development of a SLAM System for Small UAVs in Indoor Environments using Gaussian Processes. / Jeon, Young San; Choi, Jongeun; Lee, Jeong Oog.

In: Journal of Institute of Control, Robotics and Systems, Vol. 20, No. 11, 01.11.2014, p. 1098-1102.

Research output: Contribution to journalArticle

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