Many applications, such as surveying, autonomous navigation, robotics and augmented reality, require accurate pose information with respect to a reference frame. Visual localization can complement GNSS positioning, whose availability and accuracy are often limited. However, the accuracy potential of visual localization has not yet been systematically investigated against survey-grade requirements, and no publicly available large-scale outdoor dataset provides ground-truth poses accurate enough for such an investigation. We address both gaps. First, we present the FHNW Muttenz dataset. It covers a contiguous 10 km street network, mapped in two mobile mapping campaigns about 1.5 years apart, and comprises high-resolution reference imagery as well as query sequences from four cameras in five scenes. The two campaigns are co-registered to each other with an accuracy of 5 mm. Co-registration of the query images to the reference imagery yields ground-truth poses accurate to 6-9 mm in position and well below 0.1° in orientation. All images are georeferenced with an accuracy of about 2.5 cm. Second, we use this dataset to systematically evaluate sparse feature-based visual localization with a modular pipeline built from established components, using the high-resolution reference imagery directly as the scene representation. For daytime imagery and pre-calibrated cameras, the median pose errors with respect to the reference imagery are 1-5 cm in translation and 0.03-0.1° in rotation. Given an accurately georeferenced scene representation, visual localization not only approaches the accuracy of survey-grade GNSS positioning but also provides full orientation information. The dataset is publicly available at https://fhnw-muttenz-vl-dataset.github.io/

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