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This paper presents methods for determining the position of a robotic utility vehicle to sub-meter accuracy without the use of GPS. The approach we use is ideally suited for specialty agriculture applications such as orchards, where commercially available high-accuracy GPS systems are cost-prohibitive and GPS signal interference due to tree canopy can produce unreliable results. Solving the positioning problem provides a foundation for other tasks in precision agriculture that can be conducted with autonomous or partially-automated vehicles. Our algorithms use an Extended Kalman Filter with a suite of sensors. Given an initial estimate of vehicle position, sensors on the wheels and steering linkage are used to predict the path traveled, and then a scanning laser range finder is used to correct this predicted position by measuring the relative position between the vehicle and landmarks in the field. We have experimented with intentionally placed landmarks that use reflective tape, which can easily be identified with the laser. In this paper we present the motivation behind our techniques, the specifics of the algorithms we use, the experimental setups, and the results of field tests conducted during the summer of 2009 from apple orchards in Pennsylvania. Our results provide sub-meter accuracy, and suggest strong promise for reliable localization solutions for commercial applications.