A public benchmark dataset collected in the dense urban center of the city of Austin, TX is introduced for evaluation of multi-sensor GNSS-based urban positioning. Existing public datasets on localization and/or odometry evaluation are based on sensors such as lidar, cameras, and radar. The role of GNSS in these datasets is typically limited to the generation of a reference trajectory in conjunction with a high-end inertial navigation system (INS). In contrast, the dataset introduced in this paper provides raw ADC output of wideband intermediate frequency (IF) GNSS data along with tightly synchronized raw measurements from inertial measurement units (IMUs) and a stereoscopic camera unit. This dataset will enable optimization of the full GNSS stack from signal tracking to state estimation, as well as sensor fusion with other automotive sensors. The dataset is available under Public Datasets. Efforts to collect and share similar datasets from a number of dense urban centers around the world are under way.

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Narula, Lakshay, Daniel M. LaChapelle, Matthew J. Murrian, J. Michael Wooten, Todd E. Humphreys, Elliot de Toldi, Guirec Morvant, and Jean-Baptiste Lacambre, "TEX-CUP: The University of Texas Challenge for Urban Positioning," In 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS), pp. 277-284. IEEE, 2020.