Bounded Uncertainty Localization

Camillo J. Taylor

Localization is a critical base level capability that underlies a variety of applications in robotics,computer vision and sensor networks. This talk will describe an approach to localizing a set of nodes based on available range and bearing measurements.

Conceptually, the idea is that range and bearing measurements induce constraints on the configuration space of the ensemble. Taken together, these constraints define a feasible region in this space that represents the set of formations that are consistent with all of the available sensor measurements.The scheme produces bounded uncertainty estimates for the relative configuration of the nodes by using modern convex optimization techniques to approximate the projection of this feasible region onto various subspaces of the configuration space.

The talk will describe how the localization schemes can be applied to robotics applications and how they are being extended for use on smart camera sensor networks.