Extrinsic Parameter Calibration for Line Scanning Cameras on Ground Vehicles with Navigation Systems Using a Calibration Pattern

TL;DR

This paper introduces a novel calibration method for line scanning cameras on ground vehicles, estimating their 6D pose relative to navigation systems using a calibration pattern and MCMC for uncertainty quantification.

Contribution

The paper presents a new approach for calibrating line scanning cameras' 6D pose using calibration patterns and triangulation, with uncertainty estimation via MCMC.

Findings

Achieved pose estimation accuracy within 0.06 m / 1.05° and 0.18 m / 2.39° on tested platforms.

Proposed visualization methods improve human interpretability of 6D calibration results.

Method effectively integrates camera and navigation data for precise georeferencing.

Abstract

Line scanning cameras, which capture only a single line of pixels, have been increasingly used in ground based mobile or robotic platforms. In applications where it is advantageous to directly georeference the camera data to world coordinates, an accurate estimate of the camera's 6D pose is required. This paper focuses on the common case where a mobile platform is equipped with a rigidly mounted line scanning camera, whose pose is unknown, and a navigation system providing vehicle body pose estimates. We propose a novel method that estimates the camera's pose relative to the navigation system. The approach involves imaging and manually labelling a calibration pattern with distinctly identifiable points, triangulating these points from camera and navigation system data and reprojecting them in order to compute a likelihood, which is maximised to estimate the 6D camera pose. Additionally, a Markov Chain Monte Carlo (MCMC) algorithm is used to estimate the uncertainty of the offset. Tested on two different platforms, the method was able to estimate the pose to within 0.06 m / 1.05$^{\circ}$ and 0.18 m / 2.39$^{\circ}$. We also propose several approaches to displaying and interpreting the 6D results in a human readable way.