Approximating UAV and vision feature point correlations in a simplified SLAM problem

Jeffrey A. Lewis, Eric N. Johnson

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

1 Scopus citations

Abstract

Navigation with a range sensor and vision aided inertial measurement unit (IMU) estimation is difficult in Global Positioning System (GPS) denied environments. Ignoring vision feature point and vehicle state correlations contributes to inaccuracy and filter inconsistency. Approximation of feature point and vehicle cross correlation terms would allow the accuracy and consistency comparable to a correlated solution whilst reducing operation count and allowing for decoupled filter design. A Monte-Carlo simulation for a two dimensional bearing to feature point approximation of the simultaneous localization and mapping (SLAM) problem was developed. The results of a least absolute shrinkage and selection operator (LASSO) regression were then used to estimate cross covariance terms. A 1000 trial simulation showed that the regression solution was comparable in accuracy and consistency to the fully correlated solution. Future developments have the potential to provide a more accurate, approximately correlated SLAM solution to bound IMU drift for UAVs operating in a GPS denied environment.

Original languageEnglish (US)
Title of host publication2019 International Conference on Unmanned Aircraft Systems, ICUAS 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1381-1388
Number of pages8
ISBN (Electronic)9781728103327
DOIs
StatePublished - Jun 2019
Event2019 International Conference on Unmanned Aircraft Systems, ICUAS 2019 - Atlanta, United States
Duration: Jun 11 2019Jun 14 2019

Publication series

Name2019 International Conference on Unmanned Aircraft Systems, ICUAS 2019

Conference

Conference2019 International Conference on Unmanned Aircraft Systems, ICUAS 2019
Country/TerritoryUnited States
CityAtlanta
Period6/11/196/14/19

All Science Journal Classification (ASJC) codes

  • Aerospace Engineering
  • Control and Optimization

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