A square root inverse filter for efficient vision-aided inertial navigation on mobile devices

Kejian Wu; Ahmed M. Ahmed; Georgios A. Georgiou; Stergios Roumeliotis

doi:10.15607/RSS.2015.XI.008

A square root inverse filter for efficient vision-aided inertial navigation on mobile devices

Kejian Wu, Ahmed M. Ahmed, Georgios A. Georgiou, Stergios Roumeliotis

Computer Science and Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

45 Scopus citations

Abstract

In this paper, we present a square-root inverse sliding window filter (SR-ISWF) for vision-aided inertial navigation systems (VINS). While regular inverse filters suffer from numerical issues, employing their square-root equivalent enables the usage of single-precision number representations, thus achieving considerable speed ups as compared to double-precision alternatives on resource-constrained mobile platforms. Besides a detailed description of the SR-ISWF for VINS, which focuses on the numerical procedures that enable exploiting the problem's structure for gaining in efficiency, this paper presents a thorough validation of the algorithm's processing requirements and achieved accuracy. In particular, experiments are conducted using a commercial-grade cell phone, where the proposed algorithm is shown to achieve the same level of estimation accuracy, when compared to state-of-the-art VINS algorithms, with significantly higher speed.

Original language	English (US)
Title of host publication	Robotics
Subtitle of host publication	Science and Systems XI, RSS 2015
Editors	Jonas Buchli, David Hsu, Lydia E. Kavraki
Publisher	MIT Press Journals
ISBN (Electronic)	9780992374716
DOIs	https://doi.org/10.15607/RSS.2015.XI.008
State	Published - 2015
Event	2015 Robotics: Science and Systems Conference, RSS 2015 - Rome, Italy Duration: Jul 13 2015 → Jul 17 2015

Publication series

Name	Robotics: Science and Systems
Volume	11
ISSN (Electronic)	2330-765X

Other

Other	2015 Robotics: Science and Systems Conference, RSS 2015
Country	Italy
City	Rome
Period	7/13/15 → 7/17/15

Bibliographical note

Funding Information:
This work was supported by the University of Minnesota through the Digital Technology Center (DTC), AFOSR (FA9550-10-1-0567), and the National Science Foundation (IIS-1328722).

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A square root inverse filter for efficient vision-aided inertial navigation on mobile devices. / Wu, Kejian; Ahmed, Ahmed M.; Georgiou, Georgios A.; Roumeliotis, Stergios.

Robotics: Science and Systems XI, RSS 2015. ed. / Jonas Buchli; David Hsu; Lydia E. Kavraki. MIT Press Journals, 2015. (Robotics: Science and Systems; Vol. 11).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Wu, K, Ahmed, AM, Georgiou, GA & Roumeliotis, S 2015, A square root inverse filter for efficient vision-aided inertial navigation on mobile devices. in J Buchli, D Hsu & LE Kavraki (eds), Robotics: Science and Systems XI, RSS 2015. Robotics: Science and Systems, vol. 11, MIT Press Journals, 2015 Robotics: Science and Systems Conference, RSS 2015, Rome, Italy, 7/13/15. https://doi.org/10.15607/RSS.2015.XI.008

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abstract = "In this paper, we present a square-root inverse sliding window filter (SR-ISWF) for vision-aided inertial navigation systems (VINS). While regular inverse filters suffer from numerical issues, employing their square-root equivalent enables the usage of single-precision number representations, thus achieving considerable speed ups as compared to double-precision alternatives on resource-constrained mobile platforms. Besides a detailed description of the SR-ISWF for VINS, which focuses on the numerical procedures that enable exploiting the problem's structure for gaining in efficiency, this paper presents a thorough validation of the algorithm's processing requirements and achieved accuracy. In particular, experiments are conducted using a commercial-grade cell phone, where the proposed algorithm is shown to achieve the same level of estimation accuracy, when compared to state-of-the-art VINS algorithms, with significantly higher speed.",

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