# Tight Fusion of Events and Inertial Measurements for Direct Velocity Estimation

arXiv: https://arxiv.org/abs/2401.09296 · HTML: https://mlsanity.com/paper/2401.09296/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 72 references that are in the map.
- First posted on arXiv 17 Jan 2024.

## Builds on (most cited first, 25 of 25)

- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [ORB-SLAM: a Versatile and Accurate Monocular SLAM System](https://mlsanity.com/paper/1502.00956/) (2015)
- [ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM](https://mlsanity.com/paper/2007.11898/) (2020)
- [VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator](https://mlsanity.com/paper/1708.03852/) (2017)
- [Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age](https://mlsanity.com/paper/1606.05830/) (2016)
- [Event-based Vision meets Deep Learning on Steering Prediction for Self-driving Cars](https://mlsanity.com/paper/1804.01310/) (2018)
- [A Unifying Contrast Maximization Framework for Event Cameras, with Applications to Motion, Depth, and Optical Flow Estimation](https://mlsanity.com/paper/1804.01306/) (2018)
- [Go-ICP: A Globally Optimal Solution to 3D ICP Point-Set Registration](https://mlsanity.com/paper/1605.03344/) (2016)
- [Ultimate SLAM? Combining Events, Images, and IMU for Robust Visual SLAM in HDR and High Speed Scenarios](https://mlsanity.com/paper/1709.06310/) (2017)
- [Event-based Stereo Visual Odometry](https://mlsanity.com/paper/2007.15548/) (2020)
- [On-Manifold Preintegration for Real-Time Visual-Inertial Odometry](https://mlsanity.com/paper/1512.02363/) (2015)
- [Event-aided Direct Sparse Odometry](https://mlsanity.com/paper/2204.07640/) (2022)
- [Focus Is All You Need: Loss Functions For Event-based Vision](https://mlsanity.com/paper/1904.07235/) (2019)
- [Continuous-Time Visual-Inertial Odometry for Event Cameras](https://mlsanity.com/paper/1702.07389/) (2017)
- [Event-based, 6-DOF Camera Tracking from Photometric Depth Maps](https://mlsanity.com/paper/1607.03468/) (2016)
- [Event-Based Angular Velocity Regression with Spiking Networks](https://mlsanity.com/paper/2003.02790/) (2020)
- [DEVO: Depth-Event Camera Visual Odometry in Challenging Conditions](https://mlsanity.com/paper/2202.02556/) (2022)
- [Globally Optimal Contrast Maximisation for Event-based Motion Estimation](https://mlsanity.com/paper/2002.10686/) (2020)
- [Globally-Optimal Contrast Maximisation for Event Cameras](https://mlsanity.com/paper/2206.05127/) (2022)
- [IDOL: A Framework for IMU-DVS Odometry using Lines](https://mlsanity.com/paper/2008.05749/) (2020)
- [DM-VIO: Delayed Marginalization Visual-Inertial Odometry](https://mlsanity.com/paper/2201.04114/) (2022)
- [High Speed Event Camera TRacking](https://mlsanity.com/paper/2010.02771/) (2020)
- [Canny-VO: Visual Odometry with RGB-D Cameras based on Geometric 3D-2D Edge Alignment](https://mlsanity.com/paper/2012.08228/) (2020)
- [Continuous Event-Line Constraint for Closed-Form Velocity Initialization](https://mlsanity.com/paper/2109.04313/) (2021)
- [VOLDOR-SLAM: For the Times When Feature-Based or Direct Methods Are Not Good Enough](https://mlsanity.com/paper/2104.06800/) (2021)

## Built on by (most cited first, 6 of 6)

- [Full-DoF Egomotion Estimation for Event Cameras Using Geometric Solvers](https://mlsanity.com/paper/2503.03307/) (2025)
- [EVIT: Event-based Visual-Inertial Tracking in Semi-Dense Maps Using Windowed Nonlinear Optimization](https://mlsanity.com/paper/2408.01370/) (2024)
- [A Linear N-Point Solver for Structure and Motion from Asynchronous Tracks](https://mlsanity.com/paper/2507.22733/) (2025)
- [eKalibr: Dynamic Intrinsic Calibration for Event Cameras From First Principles of Events](https://mlsanity.com/paper/2501.05688/) (2025)
- [On the Benefits of Visual Stabilization for Frame- and Event-based Perception](https://mlsanity.com/paper/2408.15602/) (2024)
- [Radar and Event Camera Fusion for Agile Robot Ego-Motion Estimation](https://mlsanity.com/paper/2506.18443/) (2025)

## Abstract (arXiv)

Traditional visual-inertial state estimation targets absolute camera poses and spatial landmark locations while first-order kinematics are typically resolved as an implicitly estimated sub-state. However, this poses a risk in velocity-based control scenarios, as the quality of the estimation of kinematics depends on the stability of absolute camera and landmark coordinates estimation. To address this issue, we propose a novel solution to tight visual-inertial fusion directly at the level of first-order kinematics by employing a dynamic vision sensor instead of a normal camera. More specifically, we leverage trifocal tensor geometry to establish an incidence relation that directly depends on events and camera velocity, and demonstrate how velocity estimates in highly dynamic situations can be obtained over short time intervals. Noise and outliers are dealt with using a nested two-layer RANSAC scheme. Additionally, smooth velocity signals are obtained from a tight fusion with pre-integrated inertial signals using a sliding window optimizer. Experiments on both simulated and real data demonstrate that the proposed tight event-inertial fusion leads to continuous and reliable velocity estimation in highly dynamic scenarios independently of absolute coordinates. Furthermore, in extreme cases, it achieves more stable and more accurate estimation of kinematics than traditional, point-position-based visual-inertial odometry.