HybVIO: Pushing the limits of real-time visual-inertial odometry

Otto Seiskari*, Pekka Rantalankila, Juho Kannala, Jerry Ylilammi, Esa Rahtu, Arno Solin

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

14 Citations (Scopus)

Abstract

We present HybVIO, a novel hybrid approach for combining filtering-based visual-inertial odometry (VIO) with optimization-based SLAM. The core of our method is highly robust, independent VIO with improved IMU bias modeling, outlier rejection, stationarity detection, and feature track selection, which is adjustable to run on embedded hardware. Long-term consistency is achieved with a loosely-coupled SLAM module. In academic benchmarks, our solution yields excellent performance in all categories, especially in the real-time use case, where we outperform the current state-of-the-art. We also demonstrate the feasibility of VIO for vehicular tracking on consumer-grade hardware using a custom dataset, and show good performance in comparison to current commercial VISLAM alternatives.
Original languageEnglish
Title of host publicationProceedings - 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
PublisherIEEE
Pages287-296
Number of pages10
ISBN (Electronic)978-1-6654-0915-5
ISBN (Print)978-1-6654-0916-2
DOIs
Publication statusPublished - 2022
MoE publication typeA4 Conference publication
EventIEEE Winter Conference on Applications of Computer Vision - Waikoloa, United States
Duration: 4 Jan 20228 Jan 2022
Conference number: 22

Publication series

NameIEEE Winter Conference on Applications of Computer Vision
ISSN (Electronic)2642-9381

Conference

ConferenceIEEE Winter Conference on Applications of Computer Vision
Abbreviated titleWACV
Country/TerritoryUnited States
CityWaikoloa
Period04/01/202208/01/2022

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