Online Extrinsic Calibration based on Per-Sensor Ego-Motion Using Dual Quaternions

TL;DR

This paper introduces an online extrinsic sensor calibration method using dual quaternions, providing globally optimal and fast local solutions, validated on simulated and real datasets including KITTI, with high accuracy and real-time performance.

Contribution

The paper presents a novel dual quaternion-based approach for online extrinsic calibration that guarantees global optimality and includes methods for solution verification and prior knowledge integration.

Findings

Achieves state-of-the-art accuracy in sensor calibration.

Operates in real-time with short run times.

Validated on KITTI and other datasets, confirming robustness.

Abstract

In this work, we propose an approach for extrinsic sensor calibration from per-sensor ego-motion estimates. Our problem formulation is based on dual quaternions, enabling two different online capable solving approaches. We provide a certifiable globally optimal and a fast local approach along with a method to verify the globality of the local approach. Additionally, means for integrating previous knowledge, for example, a common ground plane for planar sensor motion, are described. Our algorithms are evaluated on simulated data and on a publicly available dataset containing RGB-D camera images. Further, our online calibration approach is tested on the KITTI odometry dataset, which provides data of a lidar and two stereo camera systems mounted on a vehicle. Our evaluation confirms the short run time, state-of-the-art accuracy, as well as online capability of our approach while retaining the global optimality of the solution at any time.