HybVIO: Pushing the Limits of Real-time Visual-inertial Odometry
This work addresses real-time localization and mapping for applications like robotics or autonomous vehicles, representing an incremental improvement with hybrid integration.
The paper tackles the problem of real-time visual-inertial odometry by introducing HybVIO, a hybrid method combining filtering-based VIO with optimization-based SLAM, which outperforms state-of-the-art in benchmarks and demonstrates feasibility on consumer hardware.
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. An open-source implementation of the HybVIO method is available at https://github.com/SpectacularAI/HybVIO