  Research in Simultaneous Localization and Mapping (SLAM) has made outstanding
progress over the past years. SLAM systems are nowadays transitioning from
academic to real world applications. However, this transition has posed new
demanding challenges in terms of accuracy and robustness. To develop new SLAM
systems that can address these challenges, new datasets containing cutting-edge
hardware and realistic scenarios are required. We propose the Hilti SLAM
Challenge Dataset. Our dataset contains indoor sequences of offices, labs, and
construction environments and outdoor sequences of construction sites and
parking areas. All these sequences are characterized by featureless areas and
varying illumination conditions that are typical in real-world scenarios and
pose great challenges to SLAM algorithms that have been developed in confined
lab environments. Accurate sparse ground truth, at millimeter level, is
provided for each sequence. The sensor platform used to record the data
includes a number of visual, lidar, and inertial sensors, which are spatially
and temporally calibrated. The purpose of this dataset is to foster the
research in sensor fusion to develop SLAM algorithms that can be deployed in
tasks where high accuracy and robustness are required, e.g., in construction
environments. Many academic and industrial groups tested their SLAM systems on
the proposed dataset in the Hilti SLAM Challenge. The results of the challenge,
which are summarized in this paper, show that the proposed dataset is an
important asset in the development of new SLAM algorithms that are ready to be
deployed in the real-world.
