{"url":"/dataset/ard-16-dataset","name":"ARD-16","full_name":"Ati Real-world Dataset","description_markdown":"We create ARD-16 (Ati Realworld Dataset), a\nfirst of its kind real-world paired correspondence dataset, by applying our dataset generation method on 16-beam VLP-16 Puck LiDAR scans on a slow-moving Unmanned Ground Vehicle. We obtain ground truth poses by using fine resolution brute force scan matching, similar to Google's Cartographer. It was captured in outdoor environment at Robert Bosch centre, IISc\nwith no moving objects during static run and several moving objects (1 car, 1 2-wheeler, few pedestrians) during dynamic run. It consists of 1.5k scans/run and we collected 10 dynamic and 5 static runs. This gives about 14k LiDAR scan pairs\nfor training, validation and testing.","description_withheld":null,"homepage":"","introduced_date":"2021-05-26","introduced_date_note":null,"introduced_by":{"paper":"/paper/dslr-dynamic-to-static-lidar-scan","title":"DSLR: Dynamic to Static LiDAR Scan Reconstruction Using Adversarially Trained Autoencoder","first_author":"Prashant Kumar","url":null},"license":null,"modalities":[],"tasks":[],"languages":[],"variants":["ARD-16"],"data_loaders":[],"num_papers_in_archive":4,"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."}