By Topic

Automated controlled imagery capture in urban environments

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

1 Author(s)
S. Teller ; Lab. for Comput. Sci., MIT, Cambridge, MA, USA

We describe the design considerations underlying a system for fully automated capture of precisely controlled imagery in urban scenes. The system operates for architectural scenes in which, from every camera position, some two vanishing points are visible. It has been used to capture thousands of controlled images in outdoor environments spanning hundreds of meters. The proposed system architecture forms the foundation for a future, fully robotic outdoor mapping capability for urban areas,. analogous to existing, satellite-based robotic mapping systems which acquire images and models of natural terrain. Four key ideas distinguish our approach from other methods. First, our sensor acquires georeferencing metadata with every image, enabling related images to be efficiently identified and registered. Second, the sensor acquires omnidirectional images; we show strong experimental evidence that such images are fundamentally more powerful observations than conventional (narrow-FOV) images. Third, the system uses a probabilistic, projective-error formulation to account for uncertainty. By treating measurement error in an appropriate depth-free framework, and by deferring decisions about camera calibration and scene structure until many noisy observations can be fused, the system achieves superior robustness and accuracy. Fourth, the system's computational requirements scale linearly in the input size, the area of the acquisition region and the size of the output model. This is in contrast to many previous methods, which either assume constant-size inputs or exhibit quadratic running time (or worse) asymptotically.

Published in:

Information Fusion, 2002. Proceedings of the Fifth International Conference on  (Volume:2 )

Date of Conference:

8-11 July 2002