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We present HybVIO, a novel hybrid approach for combining filtering-basedvisual-inertial odometry (VIO) with optimization-based SLAM. The core of ourmethod is highly robust, independent VIO with improved IMU bias modeling,outlier rejection, stationarity detection, and feature track selection, whichis adjustable to run on embedded hardware. Long-term consistency is achievedwith a loosely-coupled SLAM module. In academic benchmarks, our solution yieldsexcellent performance in all categories, especially in the real-time use case,where we outperform the current state-of-the-art. We also demonstrate thefeasibility of VIO for vehicular tracking on consumer-grade hardware using acustom dataset, and show good performance in comparison to current commercialVISLAM alternatives. An open-source implementation of the HybVIO method isavailable at https://github.com/SpectacularAI/HybVIO

HybVIO: Pushing the Limits of Real-time Visual-inertial Odometry