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Simultaneous Localization and Mapping (SLAM) is being deployed in real-world
applications, however many state-of-the-art solutions still struggle in many
common scenarios. A key necessity in progressing SLAM research is the
availability of high-quality datasets and fair and transparent benchmarking. To
this end, we have created the Hilti-Oxford Dataset, to push state-of-the-art
SLAM systems to their limits. The dataset has a variety of challenges ranging
from sparse and regular construction sites to a 17th century neoclassical
building with fine details and curved surfaces. To encourage multi-modal SLAM
approaches, we designed a data collection platform featuring a lidar, five
cameras, and an IMU (Inertial Measurement Unit). With the goal of benchmarking
SLAM algorithms for tasks where accuracy and robustness are paramount, we
implemented a novel ground truth collection method that enables our dataset to
accurately measure SLAM pose errors with millimeter accuracy. To further ensure
accuracy, the extrinsics of our platform were verified with a
micrometer-accurate scanner, and temporal calibration was managed online using
hardware time synchronization. The multi-modality and diversity of our dataset
attracted a large field of academic and industrial researchers to enter the
second edition of the Hilti SLAM challenge, which concluded in June 2022. The
results of the challenge show that while the top three teams could achieve an
accuracy of 2cm or better for some sequences, the performance dropped off in
more difficult sequences.
