Infrastructure-Based Multi-camera Calibration Using Radial Projections
Paper in proceedings, 2020

Multi-camera systems are an important sensor platform for intelligent systems such as self-driving cars. Pattern-based calibration techniques can be used to calibrate the intrinsics of the cameras individually. However, extrinsic calibration of systems with little to no visual overlap between the cameras is a challenge. Given the camera intrinsics, infrastructure-based calibration techniques are able to estimate the extrinsics using 3D maps pre-built via SLAM or Structure-from-Motion. In this paper, we propose to fully calibrate a multi-camera system from scratch using an infrastructure-based approach. Assuming that the distortion is mainly radial, we introduce a two-stage approach. We first estimate the camera-rig extrinsics up to a single unknown translation component per camera. Next, we solve for both the intrinsic parameters and the missing translation components. Extensive experiments on multiple indoor and outdoor scenes with multiple multi-camera systems show that our calibration method achieves high accuracy and robustness. In particular, our approach is more robust than the naive approach of first estimating intrinsic parameters and pose per camera before refining the extrinsic parameters of the system. The implementation is available at https://github.com/youkely/InfrasCal.

Author

Yukai Lin

Swiss Federal Institute of Technology in Zürich (ETH)

Viktor Larsson

Swiss Federal Institute of Technology in Zürich (ETH)

Marcel Geppert

Swiss Federal Institute of Technology in Zürich (ETH)

Zuzana Kukelova

Czech Technical University in Prague

Marc Pollefeys

Microsoft Mixed Real & Artificial Intelligence La

Swiss Federal Institute of Technology in Zürich (ETH)

Torsten Sattler

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering, Imaging and Image Analysis

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

03029743 (ISSN) 16113349 (eISSN)

Vol. 12361 LNCS 327-344

16th European Conference on Computer Vision, ECCV 2020
Glasgow, United Kingdom,

Subject Categories

Embedded Systems

Signal Processing

Computer Vision and Robotics (Autonomous Systems)

DOI

10.1007/978-3-030-58517-4_20

More information

Latest update

12/9/2020