A Robust Extrinsic Calibration Framework for Vehicles with Unscaled Sensors

TL;DR

This paper introduces a real-time, automatic extrinsic calibration method for vehicles with unscaled sensors, capable of handling unknown scales and robust to imperfect data, eliminating the need for calibration targets.

Contribution

The authors propose a novel framework that jointly recovers scale during extrinsic calibration and enhances robustness by selecting optimal pose sets, enabling automatic calibration without prior setup.

Findings

Outperforms state-of-the-art methods in accuracy and robustness.

Operates in real time during vehicle operation.

Successfully estimates sensor scale, improving full trajectory calibration.

Abstract

Accurate extrinsic sensor calibration is essential for both autonomous vehicles and robots. Traditionally this is an involved process requiring calibration targets, known fiducial markers and is generally performed in a lab. Moreover, even a small change in the sensor layout requires recalibration. With the anticipated arrival of consumer autonomous vehicles, there is demand for a system which can do this automatically, after deployment and without specialist human expertise. To solve these limitations, we propose a flexible framework which can estimate extrinsic parameters without an explicit calibration stage, even for sensors with unknown scale. Our first contribution builds upon standard hand-eye calibration by jointly recovering scale. Our second contribution is that our system is made robust to imperfect and degenerate sensor data, by collecting independent sets of poses and automatically selecting those which are most ideal. We show that our approach's robustness is essential for the target scenario. Unlike previous approaches, ours runs in real time and constantly estimates the extrinsic transform. For both an ideal experimental setup and a real use case, comparison against these approaches shows that we outperform the state-of-the-art. Furthermore, we demonstrate that the recovered scale may be applied to the full trajectory, circumventing the need for scale estimation via sensor fusion.