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The growing world population calls for more efficient and sustainable farming technologies. Automating agricultural tasks has great potential to improve farming technologies.A key requirement for full automation is the ability of agricultural vehicles to accurately navigate entire fields without damaging value crops. One important precondition for autonomous navigation is localization, that is, the ability of a vehicle to accurately estimate its pose relative to the crops. A majority of localization approaches detect crop rows to track the heading and lateral offset of the vehicle. This is sufficient to guide the vehicle along crop rows while driving inside the field. However, switching between rows requires a longitudinal pose estimate to determine when to turn at the end of the field.Additionally, at the end of the field sensor data contains less crop row structure and more noise from wild growing vegetation. This can lead to false-positive crop row detections. In this paper, we present a localization approach that goes beyond state-of-the-art crop row following algorithms by providing robust pose estimates not only inside the field but also at the end of the field. The underlying concept of our approach is to estimate the vehicle pose relative to a global navigation satellite system (GNSS)-referenced map of crop rows. This allows us to fuse crop row detections with GNSS signals to obtain a pose estimate with the accuracy comparable to a row following approach in the heading and lateral offset, while at the same time maintaining at least GNSS accuracy along the row.Employing a GNSS-referenced map of crop rows poses several challenges. To relate the detected crop rows to those in the map, we propose a data association strategy that finds correspondences between two sets of lines, that is, crop rows. Furthermore, we improve the GNSS-based longitudinal pose estimate by detecting the end of the field from vegetation data. Additionally, we introduce a novel method to determine false-positive crop row detections to increase the overall robustness in particular in challenging scenarios at the end of the field. Extensive real-world experiments on three different types of crops demonstrate that our localization approach is well suited for fully autonomous navigation in entire fields.

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