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

TL;DR

HybVIO introduces a robust hybrid visual-inertial odometry system that combines filtering and optimization techniques, achieving real-time performance and long-term accuracy suitable for embedded and vehicular applications.

Contribution

The paper presents a novel hybrid VIO approach integrating filtering-based VIO with optimization-based SLAM, enhancing robustness, accuracy, and real-time capability on embedded hardware.

Findings

Outperforms state-of-the-art in real-time benchmarks

Demonstrates effective vehicular tracking on consumer hardware

Achieves long-term consistency with a loosely-coupled SLAM module

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. An open-source implementation of the HybVIO method is available at https://github.com/SpectacularAI/HybVIO