  Mapping and localization is a critical module of autonomous driving, and
significant achievements have been reached in this field. Beyond Global
Navigation Satellite System (GNSS), research in point cloud registration,
visual feature matching, and inertia navigation has greatly enhanced the
accuracy and robustness of mapping and localization in different scenarios.
However, highly urbanized scenes are still challenging: LIDAR- and camera-based
methods perform poorly with numerous dynamic objects; the GNSS-based solutions
experience signal loss and multipath problems; the inertia measurement units
(IMU) suffer from drifting. Unfortunately, current public datasets either do
not adequately address this urban challenge or do not provide enough sensor
information related to mapping and localization. Here we present UrbanLoco: a
mapping/localization dataset collected in highly-urbanized environments with a
full sensor-suite. The dataset includes 13 trajectories collected in San
Francisco and Hong Kong, covering a total length of over 40 kilometers. Our
dataset includes a wide variety of urban terrains: urban canyons, bridges,
tunnels, sharp turns, etc. More importantly, our dataset includes information
from LIDAR, cameras, IMU, and GNSS receivers. Now the dataset is publicly
available through the link in the footnote. Dataset Link:
https://advdataset2019.wixsite.com/urbanloco.
