# Online Extrinsic Calibration based on Per-Sensor Ego-Motion Using Dual Quaternions

arXiv: https://arxiv.org/abs/2101.11440 · HTML: https://mlsanity.com/paper/2101.11440/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jan 2021.

## Built on by (most cited first, 7 of 7)

- [OpenCalib: A Multi-sensor Calibration Toolbox for Autonomous Driving](https://mlsanity.com/paper/2205.14087/) (2022)
- [Extrinsic Camera Calibration with Semantic Segmentation](https://mlsanity.com/paper/2208.03949/) (2022)
- [Physics and semantic informed multi-sensor calibration via optimization theory and self-supervised learning](https://mlsanity.com/paper/2206.02856/) (2022)
- [Globally Optimal Multi-Scale Monocular Hand-Eye Calibration Using Dual Quaternions](https://mlsanity.com/paper/2201.04473/) (2022)
- [Automated Static Camera Calibration with Intelligent Vehicles](https://mlsanity.com/paper/2304.10814/) (2023)
- [WATonoBus: Field-Tested All-Weather Autonomous Shuttle Technology](https://mlsanity.com/paper/2312.00938/) (2023)
- [RLCNet: An end-to-end deep learning framework for simultaneous online calibration of LiDAR, RADAR, and Camera](https://mlsanity.com/paper/2512.08262/) (2025)

## Abstract (arXiv)

In this work, we propose an approach for extrinsic sensor calibration from per-sensor ego-motion estimates. Our problem formulation is based on dual quaternions, enabling two different online capable solving approaches. We provide a certifiable globally optimal and a fast local approach along with a method to verify the globality of the local approach. Additionally, means for integrating previous knowledge, for example, a common ground plane for planar sensor motion, are described. Our algorithms are evaluated on simulated data and on a publicly available dataset containing RGB-D camera images. Further, our online calibration approach is tested on the KITTI odometry dataset, which provides data of a lidar and two stereo camera systems mounted on a vehicle. Our evaluation confirms the short run time, state-of-the-art accuracy, as well as online capability of our approach while retaining the global optimality of the solution at any time.