  Simultaneous Localization and Mapping systems are a key enabler for
positioning in both handheld and robotic applications. The Hilti SLAM
Challenges organized over the past years have been successful at benchmarking
some of the world's best SLAM Systems with high accuracy. However, more
capabilities of these systems are yet to be explored, such as platform
agnosticism across varying sensor suites and multi-session SLAM. These factors
indirectly serve as an indicator of robustness and ease of deployment in
real-world applications. There exists no dataset plus benchmark combination
publicly available, which considers these factors combined. The Hilti SLAM
Challenge 2023 Dataset and Benchmark addresses this issue. Additionally, we
propose a novel fiducial marker design for a pre-surveyed point on the ground
to be observable from an off-the-shelf LiDAR mounted on a robot, and an
algorithm to estimate its position at mm-level accuracy. Results from the
challenge show an increase in overall participation, single-session SLAM
systems getting increasingly accurate, successfully operating across varying
sensor suites, but relatively few participants performing multi-session SLAM.
Dataset URL: https://www.hilti-challenge.com/dataset-2023.html
