# Online Temporal Calibration for Monocular Visual-Inertial Systems

arXiv: https://arxiv.org/abs/1808.00692 · HTML: https://mlsanity.com/paper/1808.00692/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Aug 2018.

## Built on by (most cited first, 26 of 26)

- [DROID-SLAM: Deep Visual SLAM for Monocular, Stereo, and RGB-D Cameras](https://mlsanity.com/paper/2108.10869/) (2021)
- [R3LIVE: A Robust, Real-time, RGB-colored, LiDAR-Inertial-Visual tightly-coupled state Estimation and mapping package](https://mlsanity.com/paper/2109.07982/) (2021)
- [OpenCalib: A Multi-sensor Calibration Toolbox for Autonomous Driving](https://mlsanity.com/paper/2205.14087/) (2022)
- [From SLAM to Situational Awareness: Challenges and Survey](https://mlsanity.com/paper/2110.00273/) (2021)
- [Open-vocabulary Mobile Manipulation in Unseen Dynamic Environments with 3D Semantic Maps](https://mlsanity.com/paper/2406.18115/) (2024)
- [Continuous-Time vs. Discrete-Time Vision-based SLAM: A Comparative Study](https://mlsanity.com/paper/2202.08894/) (2022)
- [D3FlowSLAM: Self-Supervised Dynamic SLAM with Flow Motion Decomposition and DINO Guidance](https://mlsanity.com/paper/2207.08794/) (2022)
- [HybVIO: Pushing the Limits of Real-time Visual-inertial Odometry](https://mlsanity.com/paper/2106.11857/) (2021)
- [SR-LIVO: LiDAR-Inertial-Visual Odometry and Mapping with Sweep Reconstruction](https://mlsanity.com/paper/2312.16800/) (2023)
- [LIFE: Lighting Invariant Flow Estimation](https://mlsanity.com/paper/2104.03097/) (2021)
- [CROON: Automatic Multi-LiDAR Calibration and Refinement Method in Road Scene](https://mlsanity.com/paper/2203.03182/) (2022)
- [R3LIVE++: A Robust, Real-time, Radiance reconstruction package with a tightly-coupled LiDAR-Inertial-Visual state Estimator](https://mlsanity.com/paper/2209.03666/) (2022)
- [SensorX2car: Sensors-to-car calibration for autonomous driving in road scenarios](https://mlsanity.com/paper/2301.07279/) (2023)
- [OG-Mapping: Octree-based Structured 3D Gaussians for Online Dense Mapping](https://mlsanity.com/paper/2408.17223/) (2024)
- [TrajMatch: Towards Automatic Spatio-temporal Calibration for Roadside LiDARs through Trajectory Matching](https://mlsanity.com/paper/2302.02157/) (2023)
- [Is That Rain? Understanding Effects on Visual Odometry Performance for Autonomous UAVs and Efficient DNN-based Rain Classification at the Edge](https://mlsanity.com/paper/2407.12663/) (2024)
- [P2U-SLAM: A Monocular Wide-FoV SLAM System Based on Point Uncertainty and Pose Uncertainty](https://mlsanity.com/paper/2409.10143/) (2024)
- [Towards autonomous photogrammetric forest inventory using a lightweight under-canopy robotic drone](https://mlsanity.com/paper/2501.12073/) (2025)
- [Accurate Visual-Inertial SLAM by Feature Re-identification](https://mlsanity.com/paper/2102.13438/) (2021)
- [DVIO: Depth aided visual inertial odometry for RGBD sensors](https://mlsanity.com/paper/2110.10805/) (2021)
- [Joint Spatial-Temporal Calibration for Camera and Global Pose Sensor](https://mlsanity.com/paper/2403.00976/) (2024)
- [Lost in Tracking Translation: A Comprehensive Analysis of Visual SLAM in Human-Centered XR and IoT Ecosystems](https://mlsanity.com/paper/2411.07146/) (2024)
- [Universal Online Temporal Calibration for Optimization-based Visual-Inertial Navigation Systems](https://mlsanity.com/paper/2501.01788/) (2025)
- [Skyshield: Event-Driven Submillimetre Thin Obstacle Detection for Drone Flight Safety](https://mlsanity.com/paper/2508.09397/) (2025)
- [DAP-Pose: Deep Temporal Alignment and Physics-aware Cross-modal Sensor Fusion for Robust Pose Estimation](https://mlsanity.com/paper/2607.23755/) (2026)
- [Diagnosing Temporal Misalignment in Multichannel Time-Series Classification with Minimum Description Length](https://mlsanity.com/paper/2609.14595/) (2026)

## Abstract (arXiv)

Accurate state estimation is a fundamental module for various intelligent applications, such as robot navigation, autonomous driving, virtual and augmented reality. Visual and inertial fusion is a popular technology for 6-DOF state estimation in recent years. Time instants at which different sensors' measurements are recorded are of crucial importance to the system's robustness and accuracy. In practice, timestamps of each sensor typically suffer from triggering and transmission delays, leading to temporal misalignment (time offsets) among different sensors. Such temporal offset dramatically influences the performance of sensor fusion. To this end, we propose an online approach for calibrating temporal offset between visual and inertial measurements. Our approach achieves temporal offset calibration by jointly optimizing time offset, camera and IMU states, as well as feature locations in a SLAM system. Furthermore, the approach is a general model, which can be easily employed in several feature-based optimization frameworks. Simulation and experimental results demonstrate the high accuracy of our calibration approach even compared with other state-of-art offline tools. The VIO comparison against other methods proves that the online temporal calibration significantly benefits visual-inertial systems. The source code of temporal calibration is integrated into our public project, VINS-Mono.