# Joint Spatial-Temporal Calibration for Camera and Global Pose Sensor

arXiv: https://arxiv.org/abs/2403.00976 · HTML: https://mlsanity.com/paper/2403.00976/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 32 references that are in the map.
- First posted on arXiv 1 Mar 2024.

## Builds on (most cited first, 8 of 8)

- [A micro Lie theory for state estimation in robotics](https://mlsanity.com/paper/1812.01537/) (2018)
- [The TUM VI Benchmark for Evaluating Visual-Inertial Odometry](https://mlsanity.com/paper/1804.06120/) (2018)
- [The Double Sphere Camera Model](https://mlsanity.com/paper/1807.08957/) (2018)
- [Efficient Derivative Computation for Cumulative B-Splines on Lie Groups](https://mlsanity.com/paper/1911.08860/) (2019)
- [Online Temporal Calibration for Monocular Visual-Inertial Systems](https://mlsanity.com/paper/1808.00692/) (2018)
- [Adaptive Robust Kernels for Non-Linear Least Squares Problems](https://mlsanity.com/paper/2004.14938/) (2020)
- [Direct Sparse Odometry with Rolling Shutter](https://mlsanity.com/paper/1808.00558/) (2018)
- [Observability-Aware Intrinsic and Extrinsic Calibration of LiDAR-IMU Systems](https://mlsanity.com/paper/2205.03276/) (2022)

## Abstract (arXiv)

In robotics, motion capture systems have been widely used to measure the accuracy of localization algorithms. Moreover, this infrastructure can also be used for other computer vision tasks, such as the evaluation of Visual (-Inertial) SLAM dynamic initialization, multi-object tracking, or automatic annotation. Yet, to work optimally, these functionalities require having accurate and reliable spatial-temporal calibration parameters between the camera and the global pose sensor. In this study, we provide two novel solutions to estimate these calibration parameters. Firstly, we design an offline target-based method with high accuracy and consistency. Spatial-temporal parameters, camera intrinsic, and trajectory are optimized simultaneously. Then, we propose an online target-less method, eliminating the need for a calibration target and enabling the estimation of time-varying spatial-temporal parameters. Additionally, we perform detailed observability analysis for the target-less method. Our theoretical findings regarding observability are validated by simulation experiments and provide explainable guidelines for calibration. Finally, the accuracy and consistency of two proposed methods are evaluated with hand-held real-world datasets where traditional hand-eye calibration method do not work.