Dual hypothesis filter for robust INS/camera fusion

Chen Chi Chu, F. Adhika Pradipta Lie, Demoz Gebre Egziabher

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

3 Scopus citations

Abstract

This paper presents a sensor fusion methodology that enhances the robustness (here defined as resistance to divergence) of filters used to mechanize camera-aided inertial navigation systems (INS). Maintaining stability of the filter that fuses camera and INS information can be challenging when low quality (consumer/automotive grade) inertial sensors are used. This is because the optimal fusion strategy between camera pixel measurements and INS relies on a non-linear measurement equation which is not well-behaved in some unfavorable landmark geometries. In these cases, the filter estimates can diverge. There are camera-INS fusion strategies which are less divergence-prone but less flexible and accurate. In view of above, it may be beneficial to design a filter that can switch between optimal and suboptimal strategies "on the fly" depending on the geometry of the landmarks being tracked and the quality of the inertial sensor. This paper proposes such a strategy based on dual hypothesis testing approach. The proposed approach has the advantages of enhancing the robustness while maintaining the estimation accuracy. The filter performance is examined and validated using simulated UAV flight data.

Original languageEnglish (US)
Title of host publicationInstitute of Navigation International Technical Meeting 2013, ITM 2013
Pages792-802
Number of pages11
StatePublished - 2013
EventInstitute of Navigation International Technical Meeting 2013, ITM 2013 - San Diego, CA, United States
Duration: Jan 28 2013Jan 30 2013

Publication series

NameInstitute of Navigation International Technical Meeting 2013, ITM 2013

Other

OtherInstitute of Navigation International Technical Meeting 2013, ITM 2013
CountryUnited States
CitySan Diego, CA
Period1/28/131/30/13

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