  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 at
http://radionavlab.ae.utexas.edu under Public Datasets. Efforts to collect and
share similar datasets from a number of dense urban centers around the world
are under way.
