A bounded uncertainty approach to cooperative localization using relative bearing constraints

This paper describes an approach to cooperative localization which finds its roots in robust estimation, employing an unknown-but-bounded error model for sensor measurements. In this framework, range and bearing measurements obtained by the robots are viewed as constraints which implicitly define a set of feasible solutions in the joint configuration space of the robot team. The scheme produces bounded uncertainty estimates for the relative configuration of the team by using convex optimization techniques to approximate the projection of this feasible set onto various subspaces of the configuration space. The scheme can also be used to localize distributed sensor nodes. An important advantage of the proposed approach is that it is able to produce bounded uncertainty estimates for the relative configuration of the robots even in the case where the relative orientations of the robots are completely unknown. This is an important practical advance since errors in relative orientation are often a major contributor to positioning uncertainty in multi-robot localization schemes.