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Landmark-based graphs are a useful and parsimonious tool for representing large scale environments. Relating landmarks by means of feedback-control algorithms encoded in a motion description language provides a level of abstraction that enables autonomous vehicles to navigate effectively by composing strings in the language to form complex strategies that would be difficult to design at the level of sensors and actuators. In such a setting, feedback control requires one to pay attention not only to sensor and actuator uncertainty, but also to the ambiguity introduced by the fact that many landmarks may look similar when using a modest set of observations. This work discusses the generation of language-based feedback control sequences for landmark-based navigation together with the problem of sensing landmarks sufficiently well to make feedback meaningful. The paper makes two contributions. First, we extend previous work to include the costs of sensing with varying degrees of accuracy. Second, we describe a Monte Carlo based approach to landmark sensing which relies on the use of particle filters. We include simulation results that illustrate our approach.