Extrinsic Infrastructure Calibration Using the Hand-Eye Robot-World Formulation

Abstract

We propose a certifiably globally optimal approach for solving the hand-eye robot-world problem supporting multiple sensors and targets at once. Further, we leverage this formulation for estimating a geo-referenced calibration of infrastructure sensors. Since vehicle motion recorded by infrastructure sensors is mostly planar, obtaining a unique solution for the respective hand-eye robot-world problem is unfeasible without incorporating additional knowledge. Hence, we extend our proposed method to include a-priori knowledge, i.e., the translation norm of calibration targets, to yield a unique solution. Our approach achieves state-of-the-art results on simulated and real-world data. Especially on real-world intersection data, our approach utilizing the translation norm is the only method providing accurate results.

References in corpus (5)

• Simultaneous Robot-World and Hand-Eye Calibration
• Traffic Surveillance Camera Calibration by 3D Model Bounding Box Alignment for Accurate Vehicle Speed Measurement
• Solving the Robot-World Hand-Eye(s) Calibration Problem with Iterative Methods
• Online Extrinsic Calibration based on Per-Sensor Ego-Motion Using Dual Quaternions
• Globally Optimal Multi-Scale Monocular Hand-Eye Calibration Using Dual Quaternions

Cited by in corpus (1)

• A Certifably Correct Algorithm for Generalized Robot-World and Hand-Eye Calibration