{"url":"/dataset/hilti-oxford-dataset","name":"Hilti-Oxford Dataset","full_name":null,"description_markdown":"This benchmark is based on the HILTI-OXFORD Dataset, which has been collected on construction sites as well as on the famous Sheldonian Theatre in Oxford, providing a large range of difficult problems for SLAM. \r\n\r\nAll 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 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.","description_withheld":null,"homepage":"https://hilti-challenge.com/dataset-2022.html","introduced_date":"2022-08-21","introduced_date_note":null,"introduced_by":{"paper":"/paper/hilti-oxford-dataset-a-millimetre-accurate","title":"Hilti-Oxford Dataset: A Millimetre-Accurate Benchmark for Simultaneous Localization and Mapping","first_author":null,"url":null},"license":null,"modalities":[],"tasks":[],"languages":[],"variants":["Hilti-Oxford Dataset"],"data_loaders":[],"num_papers_in_archive":1,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}