Summary: Fiducial Planning for Error-Bounded Pose Estimation of a
Panoramic Camera in Large Environments
Daniel G. Aliaga Ingrid Carlbom
Lucent Technologies Bell Labs
Panoramic image sensors are becoming increasingly popular because they capture large portions of the visual field
in a single image. These cameras are particularly effective for capturing and navigating through large, complex 3D
environments. Existing vision-based camera pose algorithms are derived for standard field-of-view cameras, but
few algorithms have been proposed to take advantage of the larger field-of-view of panoramic cameras.
Furthermore, while existing camera pose estimation algorithms work well in small spaces, they do not scale well to
large, complex 3D environments consisting of a number of interconnected spaces.
Accurate and robust estimation of the position and orientation of image sensors has been a recurring problem in
computer vision, computer graphics, and robot navigation. Stereo reconstruction methods use camera pose for
extracting depth information to reconstruct a 3D environment [12, 16]. Image-based rendering techniques [1, 3, 15,
19, 20, 28] require camera position and orientation to recreate novel views of an environment from a large number
of images. Augmented reality systems  use camera pose information to align virtual objects with real objects, and
robot navigation and localization methods [7, 8, 10, 30] must be able to obtain the robot's current location in order
to maneuver through a (captured) space.
We can divide existing vision-based camera pose approaches into passive methods and active methods. Passive