INTRODUCTION Autonomous vehicles are leading a sensing revolution in precision agriculture. Unmanned ground vehicles reduce agricultural labor needs and im- prove the precision with which the fields are managed. Highly mobile unmanned aerial vehicles (UAVs) have been used to produce highaltitude, on-demand aerial imagery of fields, in a variety of light wavelengths, allowing producers to quickly detect pest infestations, weeds, and other harm-ful environmental conditions. We believe UAVs, such as the one in Figure 1, will use improved sensors and algorithms to go from highaltitude operation far above the crops, to low- altitude autonomous operation within 1 m of crops in a field. Lowaltitude operation improves the spatial resolution of sensor data, enables the use of shorter range, cheaper, and lighter sensors (Solari, Shanahan, Ferguson, Schepers, & Gitelson, 2008), and makes physical interaction with the en-vironment possible. These changes will allow smaller, lower cost, and easier-to-operate UAVs to measure crop properties in unstructured agricultural settings. Safe operation of these next-generation UAVs requires precise localization that is not possible with current systems’ reliance on low precision GPS sensors. UAVs’ high mobility and ability to fly above the crops make them an excellent tool for collecting data from fields. However, their limited payload capacity and a need for pre- cise localization in field environments present challenges tousing UAVs for field research. In this work, we developa system and algorithms that use a laser scanner as botha means of precisely localizing a UAV in a field and as a mechanism for collecting scientific data. The localization is more accurate than standard GPS systems, and has lower cost than precise real-time kinematic (RTK) GPS solutions. Furthermore, our system does not require a predefined fieldmap, which is not always available, and dynamically reactsto field conditions in flight. The laser-based system robustly copes with the chaotic field environment and unpredictablelighting conditions that camera-based visual odometry so-lutions struggle with. A specific example of the utility of UAVs that motivates our work is corn phenotyping trials, such as those shown in Figure 2. A field will contain many different genetic varieties. The field is divided into dozens or hundredsof subplots, consisting of two rows, spaced 0.762 m (30 in) apart, for a short distance (10–15 m). Each field may con- tain the same variety planted multiple times, to increase the population size for statistical analysis. Additionally, the subplots have different treatments, such as different irrigation or fertilizer application rates, to analyze the response of the different varieties to these test conditions. Manual measurements and ground vehicles measure the plants’ response to environmental stimuli throughout the growing UAV operating in corn field. season (Busemeyer et al., 2013; Montes, Technow, Dhillon, Mauch, & Melchinger, 2011; Reis, 2013). Researchers mea- sure a wide variety of plant traits in these experiments, ranging from the root structure of corn plants to the plant height and biomass in a field at different stages of the plants’ lives. Collecting field data via manual measurements or ground vehicles has severe drawbacks. Collecting measure- ments via manual measurements is time consuming for researchers. Repeated field measurements by researchers risk damaging the plants as the researchers walk through their dense canopy. Automated test equipment mounted on ground vehicles is faster than manual measurements, but also risks damaging the plants, may compact the soil, and thus change the growing conditions, and ground vehicles cannot traverse the field in all environmental conditions. In particular, collecting data from mature fields is especially challenging, as the dense plant growth is difficult for field researchers and robots to move through. The combination of these factors severely limits the spatiotemporal resolution of phenotyping trials in large environments. In practice, the low spatiotemporal data resolution makes it difficult or impossible to accurately measure the impact of short-termenvironmental stimuli on the plants. Instead, large studies at the end of the growing season measure the combined impact of all the stimuli that the plants experienced over