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

TL;DR

This paper presents a method for calibrating vehicle sensors using stop signs in urban environments, leveraging their known shape for improved camera calibration and robustness against disturbances.

Contribution

It introduces a novel approach that uses traffic sign recognition for sensor recalibration, including detection, geometry estimation, and recursive updating.

Findings

Demonstrates convergence of calibration in natural environments

Shows improved sensor performance after calibration

Validates approach with real-world urban driving data

Abstract

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.