# Auto-calibration Method Using Stop Signs for Urban Autonomous Driving Applications

arXiv: https://arxiv.org/abs/2010.07441 · HTML: https://mlsanity.com/paper/2010.07441/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Oct 2020.

## Built on by (most cited first, 5 of 5)

- [V2XP-ASG: Generating Adversarial Scenes for Vehicle-to-Everything Perception](https://mlsanity.com/paper/2209.13679/) (2022)
- [Online,Target-Free LiDAR-Camera Extrinsic Calibration via Cross-Modal Mask Matching](https://mlsanity.com/paper/2404.18083/) (2024)
- [Environment-Driven Online LiDAR-Camera Extrinsic Calibration](https://mlsanity.com/paper/2502.00801/) (2025)
- [DST-Calib: A Dual-Path, Self-Supervised, Target-Free LiDAR-Camera Extrinsic Calibration Network](https://mlsanity.com/paper/2601.01188/) (2026)
- [Single-Frame Point-Pixel Registration via Supervised Cross-Modal Feature Matching](https://mlsanity.com/paper/2506.22784/) (2025)

## Abstract (arXiv)

Calibration of sensors is fundamental to robust performance for intelligent vehicles. In natural environments, disturbances can easily challenge calibration. One possibility is to use natural objects of known shape to recalibrate sensors. An approach based on recognition of traffic signs, such as stop signs, and use of them for recalibration of cameras is presented. The approach is based on detection, geometry estimation, calibration, and recursive updating. Results from natural environments are presented that clearly show convergence and improved performance.