Calibration of an Outdoor Distributed Camera Network with a 3D Point Cloud

Abstract

Outdoor camera networks are becoming ubiquitous in critical urban areas of the largest cities around the world. Although current applications of camera networks are mostly tailored to video surveillance, recent research projects are exploiting their use to aid robotic systems in people-assisting tasks. Such systems require precise calibration of the internal and external parameters of the distributed camera network. Despite the fact that camera calibration has been an extensively studied topic, the development of practical methods for user-assisted calibration that minimize user intervention time and maximize precision still pose significant challenges. These camera systems have non-overlapping fields of view, are subject to environmental stress, and are likely to suffer frequent recalibration. In this paper, we propose the use of a 3D map covering the area to support the calibration process and develop an automated method that allows quick and precise calibration of a large camera network. We present two cases of study of the proposed calibration method: one is the calibration of the Barcelona Robot Lab camera network, which also includes direct mappings (homographies) between image coordinates and world points in the ground plane (walking areas) to support person and robot detection and localization algorithms. The second case consist of improving the GPS positioning of geo-tagged images taken with a mobile device in the Facultat de Matemàtiques i Estadística (FME) patio at the Universitat Politècnica de Catalunya (UPC).

Uncertainty analysis of the DLT-Lines calibration algorithm for cameras with radial distortion

“…Simultaneous Localization and Map Building (SLAM), we focus our work on the aspect of calibrating A C C E P T E D M A N U S C R I P T the fixed camera with respect to the mobile camera. Our recent work on camera calibration based in point clouds [14], is extended here with a more comprehensive Gauss-Newton formulation. This formulation allows the easy introduction of additional constraints and algorithm analysis.…”

“…The uncertainty analysis is derived first for the DLT-Lines formulation presented in [14] and further extended to introduce novel cases of interest, namely the combination of DLT-Lines with square pixel constraints, and with radial distortion. Finally, the analysis is also extended for the case in which the scene can be considered to obey a floor plane constraint, typical of aerial imagery.…”