# Online Camera-to-ground Calibration for Autonomous Driving

arXiv: https://arxiv.org/abs/2303.17137 · HTML: https://mlsanity.com/paper/2303.17137/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 25 references that are in the map.
- First posted on arXiv 30 Mar 2023.

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

- [3D-CVF: Generating Joint Camera and LiDAR Features Using Cross-View Spatial Feature Fusion for 3D Object Detection](https://mlsanity.com/paper/2004.12636/) (2020)
- [Vision-Centric BEV Perception: A Survey](https://mlsanity.com/paper/2208.02797/) (2022)
- [StretchBEV: Stretching Future Instance Prediction Spatially and Temporally](https://mlsanity.com/paper/2203.13641/) (2022)
- [Understanding Bird's-Eye View of Road Semantics using an Onboard Camera](https://mlsanity.com/paper/2012.03040/) (2020)
- [Driving among Flatmobiles: Bird-Eye-View occupancy grids from a monocular camera for holistic trajectory planning](https://mlsanity.com/paper/2008.04047/) (2020)
- [AVP-SLAM: Semantic Visual Mapping and Localization for Autonomous Vehicles in the Parking Lot](https://mlsanity.com/paper/2007.01813/) (2020)
- [Monocular 3D Vehicle Detection Using Uncalibrated Traffic Cameras through Homography](https://mlsanity.com/paper/2103.15293/) (2021)
- [Pose Graph Optimization for Unsupervised Monocular Visual Odometry](https://mlsanity.com/paper/1903.06315/) (2019)

## Abstract (arXiv)

Online camera-to-ground calibration is to generate a non-rigid body transformation between the camera and the road surface in a real-time manner. Existing solutions utilize static calibration, suffering from environmental variations such as tire pressure changes, vehicle loading volume variations, and road surface diversity. Other online solutions exploit the usage of road elements or photometric consistency between overlapping views across images, which require continuous detection of specific targets on the road or assistance with multiple cameras to facilitate calibration. In our work, we propose an online monocular camera-to-ground calibration solution that does not utilize any specific targets while driving. We perform a coarse-to-fine approach for ground feature extraction through wheel odometry and estimate the camera-to-ground calibration parameters through a sliding-window-based factor graph optimization. Considering the non-rigid transformation of camera-to-ground while driving, we provide metrics to quantify calibration performance and stopping criteria to report/broadcast our satisfying calibration results. Extensive experiments using real-world data demonstrate that our algorithm is effective and outperforms state-of-the-art techniques.