We present an application of a novel framework and algorithms for: (1) conservatively and recursively incorporating information obtained through sensors that yield observations that are non-linear functions of the state; and (2) finding control inputs that continuously improve the quality of the resulting estimates. We present an experimental study of the application of our framework to mobile robots utilizing range-only sensors, and demonstrate its effectiveness in dealing with problems of target localization with one or more robots where traditional approaches involving linearization fail to consistently capture uncertainty.
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