Robust MAV State estimation using an m-estimator augmented sensor fusion graph

Derek Chen, Grace Xingxin Gao

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

Abstract

The recent surge in the MAV industry has brought many new prospective commercial applications for MAVs. Due to their versatile flight capabilities, MAVs are capable of operating in urban and human centric environments. However navigating in these environments becomes an increasingly difficult task as they are commonly GPS challenged environments as well. Low satellite visibility, multipath, and Non-Line of Sight (NLOS) errors degrade the GPS-derived navigation solution preventing fully autonomous flight. Before widespread commercial adoption of MAVs can occur, navigation within GPS-challenged environments needs to be safe and reliable. In this paper we present a sensor fusion graph augmented by m-estimators for MAV trajectory estimation in a GPS- challenged environment. We take a probabilistic approach to tight coupling of GPS and IMU sensor data. We then apply m-estimation to our sensor fusion graph in order to mitigate the multipath and NLOS errors that would otherwise skew our navigation solution. Finally we demonstrate the effectiveness of our algorithm by flight test of an As- cTec Firefly MAV, navigating from an open-skied environment into a GPS-challenged environment and estimating its trajectory.

Original languageEnglish (US)
Title of host publication28th International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS 2015
PublisherInstitute of Navigation
Pages841-848
Number of pages8
ISBN (Electronic)9781510817258
StatePublished - Jan 1 2015
Event28th International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS 2015 - Tampa, United States
Duration: Sep 14 2015Sep 18 2015

Publication series

Name28th International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS 2015
Volume2

Other

Other28th International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS 2015
CountryUnited States
CityTampa
Period9/14/159/18/15

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Software

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  • Cite this

    Chen, D., & Gao, G. X. (2015). Robust MAV State estimation using an m-estimator augmented sensor fusion graph. In 28th International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS 2015 (pp. 841-848). (28th International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS 2015; Vol. 2). Institute of Navigation.