# eKalibr-Inertial: Continuous-Time Spatiotemporal Calibration for Event-Based Visual-Inertial Systems

arXiv: https://arxiv.org/abs/2509.05923 · HTML: https://mlsanity.com/paper/2509.05923/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 36 references that are in the map.
- First posted on arXiv 7 Sep 2025.

## Builds on (most cited first, 18 of 18)

- [High Speed and High Dynamic Range Video with an Event Camera](https://mlsanity.com/paper/1906.07165/) (2019)
- [How to Calibrate Your Event Camera](https://mlsanity.com/paper/2105.12362/) (2021)
- [ESVIO: Event-based Stereo Visual Inertial Odometry](https://mlsanity.com/paper/2212.13184/) (2022)
- [Efficient Derivative Computation for Cumulative B-Splines on Lie Groups](https://mlsanity.com/paper/1911.08860/) (2019)
- [PL-EVIO: Robust Monocular Event-based Visual Inertial Odometry with Point and Line Features](https://mlsanity.com/paper/2209.12160/) (2022)
- [Deep ChArUco: Dark ChArUco Marker Pose Estimation](https://mlsanity.com/paper/1812.03247/) (2018)
- [Event-based Visual Inertial Velometer](https://mlsanity.com/paper/2311.18189/) (2023)
- [LCE-Calib: Automatic LiDAR-Frame/Event Camera Extrinsic Calibration With A Globally Optimal Solution](https://mlsanity.com/paper/2303.09825/) (2023)
- [On 'A Kalman Filter-Based Algorithm for IMU-Camera Calibration: Observability Analysis and Performance Evaluation'](https://mlsanity.com/paper/1311.4769/) (2013)
- [Robust Real-time LiDAR-inertial Initialization](https://mlsanity.com/paper/2202.11006/) (2022)
- [Observability-Aware Intrinsic and Extrinsic Calibration of LiDAR-IMU Systems](https://mlsanity.com/paper/2205.03276/) (2022)
- [iKalibr: Unified Targetless Spatiotemporal Calibration for Resilient Integrated Inertial Systems](https://mlsanity.com/paper/2407.11420/) (2024)
- [Continuous-Time Spatiotemporal Calibration of a Rolling Shutter Camera-IMU System](https://mlsanity.com/paper/2108.07200/) (2021)
- [eKalibr: Dynamic Intrinsic Calibration for Event Cameras From First Principles of Events](https://mlsanity.com/paper/2501.05688/) (2025)
- [Online Multi Camera-IMU Calibration](https://mlsanity.com/paper/2209.13821/) (2022)
- [RIs-Calib: An Open-Source Spatiotemporal Calibrator for Multiple 3D Radars and IMUs Based on Continuous-Time Estimation](https://mlsanity.com/paper/2408.02444/) (2024)
- [AsynEIO: Asynchronous Monocular Event-Inertial Odometry Using Gaussian Process Regression](https://mlsanity.com/paper/2411.12175/) (2024)
- [eKalibr-Stereo: Continuous-Time Spatiotemporal Calibration for Event-Based Stereo Visual Systems](https://mlsanity.com/paper/2504.04451/) (2025)

## Abstract (arXiv)

The bioinspired event camera, distinguished by its exceptional temporal resolution, high dynamic range, and low power consumption, has been extensively studied in recent years for motion estimation, robotic perception, and object detection. In ego-motion estimation, the visual-inertial setup is commonly adopted due to complementary characteristics between sensors (e.g., scale perception and low drift). For optimal event-based visual-inertial fusion, accurate spatiotemporal (extrinsic and temporal) calibration is required. In this work, we present eKalibr-Inertial, an accurate spatiotemporal calibrator for event-based visual-inertial systems, utilizing the widely used circle grid board. Building upon the grid pattern recognition and tracking methods in eKalibr and eKalibr-Stereo, the proposed method starts with a rigorous and efficient initialization, where all parameters in the estimator would be accurately recovered. Subsequently, a continuous-time-based batch optimization is conducted to refine the initialized parameters toward better states. The results of extensive real-world experiments show that eKalibr-Inertial can achieve accurate event-based visual-inertial spatiotemporal calibration. The implementation of eKalibr-Inertial is open-sourced at (https://github.com/Unsigned-Long/eKalibr) to benefit the research community.