As the prices of cameras and computing elements continue to fall, it
has become increasingly attractive to consider the deployment of
smart camera networks. These networks would be composed of small,
networked computers equipped with inexpensive image sensors. Such
networks could be employed in a wide range of applications including
surveillance, robotics and 3D scene reconstruction.
One critical problem that must be addressed before such systems can
be deployed effectively is the issue of localization. That is, in
order to take full advantage of the images gathered from multiple
vantage points it is helpful to know how the cameras in the scene
are positioned and oriented with respect to each other. To address
the localization problem we have proposed a novel approach to
localizing networks of embedded cameras and sensors. In this scheme
the cameras and the nodes are equipped with controllable light
sources (either visible or infrared) which are used for
signaling. Each camera node can then automatically determine the
bearing to all the nodes that are visible from its vantage point. By
fusing these measurements with the measurements obtained from
onboard accelerometers, the camera nodes are able to determine the
relative positions and orientations of other nodes in the network.
This localization technology can serve as a basic capability on
which higher level applications can be built. The method could be
used to automatically survey the locations of sensors of interest,
to implement distributed surveillance systems or to analyze the
structure of a scene based on the images obtained from multiple
registered vantage points. It also provides a mechanism for
integrating the imagery obtained from the cameras with the
measurements obtained from distributed sensors.
We have successfully used our custom made self localizing smart
camera networks to implement a novel decentralized target tracking
algorithm, create an ad-hoc range finder and localize the components
of a self assembling modular robot