# Robust Stereo Visual Inertial Odometry for Fast Autonomous Flight

arXiv: https://arxiv.org/abs/1712.00036 · HTML: https://mlsanity.com/paper/1712.00036/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Nov 2017.

## Built on by (most cited first, 27 of 27)

- [FAST-LIVO2: Fast, Direct LiDAR-Inertial-Visual Odometry](https://mlsanity.com/paper/2408.14035/) (2024)
- [Large-scale Autonomous Flight with Real-time Semantic SLAM under Dense Forest Canopy](https://mlsanity.com/paper/2109.06479/) (2021)
- [HybVIO: Pushing the Limits of Real-time Visual-inertial Odometry](https://mlsanity.com/paper/2106.11857/) (2021)
- [Recent Advancements in Deep Learning Applications and Methods for Autonomous Navigation: A Comprehensive Review](https://mlsanity.com/paper/2302.11089/) (2023)
- [The GRIFFIN Perception Dataset: Bridging the Gap Between Flapping-Wing Flight and Robotic Perception](https://mlsanity.com/paper/2101.10371/) (2021)
- [SchurVINS: Schur Complement-Based Lightweight Visual Inertial Navigation System](https://mlsanity.com/paper/2312.01616/) (2023)
- [Localization and Mapping using Instance-specific Mesh Models](https://mlsanity.com/paper/2103.04493/) (2021)
- [Monocular visual simultaneous localization and mapping: (r)evolution from geometry to deep learning-based pipelines](https://mlsanity.com/paper/2503.02955/) (2025)
- [CNN-based Ego-Motion Estimation for Fast MAV Maneuvers](https://mlsanity.com/paper/2101.01841/) (2021)
- [Expansion of Visual Hints for Improved Generalization in Stereo Matching](https://mlsanity.com/paper/2211.00392/) (2022)
- [Low Latency Visual Inertial Odometry with On-Sensor Accelerated Optical Flow for Resource-Constrained UAVs](https://mlsanity.com/paper/2406.13345/) (2024)
- [High-Speed Stereo Visual SLAM for Low-Powered Computing Devices](https://mlsanity.com/paper/2410.04090/) (2024)
- [Tightly-coupled Visual-DVL-Inertial Odometry for Robot-based Ice-water Boundary Exploration](https://mlsanity.com/paper/2303.17005/) (2023)
- [Multi-Visual-Inertial System: Analysis, Calibration and Estimation](https://mlsanity.com/paper/2308.05303/) (2023)
- [PO-MSCKF: An Efficient Visual-Inertial Odometry by Reconstructing the Multi-State Constrained Kalman Filter with the Pose-only Theory](https://mlsanity.com/paper/2407.01888/) (2024)
- [A Plug-and-Play Learning-based IMU Bias Factor for Robust Visual-Inertial Odometry](https://mlsanity.com/paper/2503.12527/) (2025)
- [A Review of Vision-Based Vehicle Detection for UAV-Based Traffic Monitoring: Experimental Insights and Future Directions](https://mlsanity.com/paper/2608.07571/) (2026)
- [Accurate Visual-Inertial SLAM by Feature Re-identification](https://mlsanity.com/paper/2102.13438/) (2021)
- [A Look at Improving Robustness in Visual-inertial SLAM by Moment Matching](https://mlsanity.com/paper/2205.13821/) (2022)
- [An Improved Multi-State Constraint Kalman Filter for Visual-Inertial Odometry](https://mlsanity.com/paper/2210.08117/) (2022)
- [Vision-based localization methods under GPS-denied conditions](https://mlsanity.com/paper/2211.11988/) (2022)
- [PathFinder: Attention-Driven Dynamic Non-Line-of-Sight Tracking with a Mobile Robot](https://mlsanity.com/paper/2404.05024/) (2024)
- [Sky-GVIO: an enhanced GNSS/INS/Vision navigation with FCN-based sky-segmentation in urban canyon](https://mlsanity.com/paper/2404.11070/) (2024)
- [Efficient and Accurate Downfacing Visual Inertial Odometry](https://mlsanity.com/paper/2509.10021/) (2025)
- [POPL-KF: A Pose-Only Geometric Representation-Based Kalman Filter for Point-Line-Based Visual-Inertial Odometry](https://mlsanity.com/paper/2602.06425/) (2026)
- [Edge-Assisted Multi-Robot Visual-Inertial SLAM with Efficient Communication](https://mlsanity.com/paper/2603.11085/) (2026)
- [MFVINS: Multiple Fisheye Camera-Based Visual Inertial System](https://mlsanity.com/paper/2609.08626/) (2026)

## Abstract (arXiv)

In recent years, vision-aided inertial odometry for state estimation has matured significantly. However, we still encounter challenges in terms of improving the computational efficiency and robustness of the underlying algorithms for applications in autonomous flight with micro aerial vehicles in which it is difficult to use high quality sensors and pow- erful processors because of constraints on size and weight. In this paper, we present a filter-based stereo visual inertial odometry that uses the Multi-State Constraint Kalman Filter (MSCKF) [1]. Previous work on stereo visual inertial odometry has resulted in solutions that are computationally expensive. We demonstrate that our Stereo Multi-State Constraint Kalman Filter (S-MSCKF) is comparable to state-of-art monocular solutions in terms of computational cost, while providing signifi- cantly greater robustness. We evaluate our S-MSCKF algorithm and compare it with state-of-art methods including OKVIS, ROVIO, and VINS-MONO on both the EuRoC dataset, and our own experimental datasets demonstrating fast autonomous flight with maximum speed of 17.5m/s in indoor and outdoor environments. Our implementation of the S-MSCKF is available at https://github.com/KumarRobotics/msckf_vio.