Globally Optimal GNSS Multi-Antenna Lever Arm Calibration

TL;DR

This paper introduces a globally optimal method for calibrating multiple GNSS antennas on autonomous vehicles using motion data, incorporating prior knowledge, and addressing planar motion challenges for accurate, real-time deployment.

Contribution

It presents a novel globally optimal calibration approach based on motion measurements, with a planar motion extension for autonomous vehicles, enabling fast and accurate online calibration.

Findings

Achieves accurate calibration results with simulated and real data.

Provides fast run times suitable for online deployment.

Addresses planar motion limitations in vehicle calibration.

Abstract

Sensor calibration is crucial for autonomous driving, providing the basis for accurate localization and consistent data fusion. Enabling the use of high-accuracy GNSS sensors, this work focuses on the antenna lever arm calibration. We propose a globally optimal multi-antenna lever arm calibration approach based on motion measurements. For this, we derive an optimization method that further allows the integration of a-priori knowledge. Globally optimal solutions are obtained by leveraging the Lagrangian dual problem and a primal recovery strategy. Generally, motion-based calibration for autonomous vehicles is known to be difficult due to cars' predominantly planar motion. Therefore, we first describe the motion requirements for a unique solution and then propose a planar motion extension to overcome this issue and enable a calibration based on the restricted motion of autonomous vehicles. Last we present and discuss the results of our thorough evaluation. Using simulated and augmented real-world data, we achieve accurate calibration results and fast run times that allow online deployment.