HybVIO : Pushing the Limits of Real-time Visual-inertial Odometry
Seiskari, Otto; Rantalankila, Pekka; Kannala, Juho; Ylilammi, Jerry; Rahtu, Esa; Solin, Arno (2022)
Seiskari, Otto
Rantalankila, Pekka
Kannala, Juho
Ylilammi, Jerry
Rahtu, Esa
Solin, Arno
IEEE
2022
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tuni-202211078207
https://urn.fi/URN:NBN:fi:tuni-202211078207
Kuvaus
Peer reviewed
Tiivistelmä
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.
Kokoelmat
- TUNICRIS-julkaisut [18936]