Offline GNSS/Camera Extrinsic Calibration Using RTK and Fiducials
Alex D. Jordan, Jacob C. Johnson, Timothy W. McLain, Randal W. Beard
Abstract
Accurate robotic state estimation often requires precise knowledge of inter-sensor offsets. In this letter we present a solution for inter-sensor calibration of systems that employ a combination of GNSS and visual-inertial sensors. RTK-GNSS and fiducial measurements are utilized to produce highly precise estimates. We present an offline batch estimation approach that utilizes a continuous-time spline on the Lie group SE(3). This approach simultaneously estimates the vehicle trajectory and sensor spatio-temporal offsets by choosing optimal spline control points and calibration parameters to solve a maximum likelihood estimation problem. Our calibration method is validated in both simulation and hardware, with comparison to an online extended Kalman filter. Hardware experiments are conducted in both a motion capture environment and outdoors. Results show that our method outperforms online methods and approaches millimeter-level accuracy.
BibTeX
@inproceedings{ral2023_offlinegnsscamer,
title = {Offline GNSS/Camera Extrinsic Calibration Using RTK and Fiducials},
author = {Alex D. Jordan and Jacob C. Johnson and Timothy W. McLain and Randal W. Beard},
booktitle = {RA-L 2023},
year = {2023}
}