HybVIO: Pushing the Limits of Real-time Visual-inertial Odometry
arXiv:2106.11857 · doi:10.1109/WACV51458.2022.00036
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
arXiv comment2022 IEEE Winter Conference on Applications of Computer Vision (WACV)
References in corpus (11)
- ORB-SLAM: a Versatile and Accurate Monocular SLAM System
- ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras
- VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator
- ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM
- On-Manifold Preintegration for Real-Time Visual-Inertial Odometry
- The TUM VI Benchmark for Evaluating Visual-Inertial Odometry
- OpenVSLAM: A Versatile Visual SLAM Framework
- Direct Sparse Visual-Inertial Odometry using Dynamic Marginalization
- Visual-Inertial Mapping with Non-Linear Factor Recovery
- PIVO: Probabilistic Inertial-Visual Odometry for Occlusion-Robust Navigation
- Accurate Monocular Visual-inertial SLAM using a Map-assisted EKF Approach