Multi-camera System Calibration with Built-in Relative Orientation Constraints (Part 1) Theoretical Principle

Abstract

In recent years, multi-camera systems have been recognized as an affordable alternative for the collection of 3D spatial data from physical surfaces. The collected data can be applied for different mapping(e.g., mobile mapping and mapping inaccessible locations)or metrology applications (e.g., industrial, biomedical, and architectural). In order to fully exploit the potential accuracy of these systems and ensure successful manipulation of the involved cameras, a careful system calibration should be performed prior to the data collection procedure. The calibration of a multi-camera system is accomplished when the individual cameras are calibrated and the geometric relationships among the different system components are defined. In this paper, a new single-step approach is introduced for the calibration of a multi-camera system (i.e., individual camera calibration and estimation of the lever-arm and boresight angles among the system components). In this approach, one of the cameras is set as the reference camera and the system mounting parameters are defined relative to that reference camera. The proposed approach is easy to implement and computationally efficient. The major advantage of this method, when compared to available multi-camera system calibration approaches, is the flexibility of being applied for either directly or indirectly geo-referenced multi-camera systems. The feasibility of the proposed approach is verified through experimental results using real data collected by a newly-developed indirectly geo-referenced multi-camera system. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http:// creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Toward an Automatic Calibration of Dual Fluoroscopy Imaging Systems

“…Usually, the calibration targets will have unique semantic and geometric properties (i.e., signalized targets) to allow for their automatic extraction/identification as well as to facilitate tasks of spatial measurement in 2D (e.g., Habib et al, 2013;Lari et al, 2014;Tommaselli et al, 2014). In the case of DF images, the calibration targets appear as black circular-like (i.e., an ellipse with low eccentricity) footprints with no semantic information, which complicates the task of automating the bundle adjustment calibration.…”