{"url":"/dataset/consinv-dataset","name":"ConsInv Dataset","full_name":null,"description_markdown":"ConsInv is a stereo RGB + IMU dataset designed for Dynamic SLAM testing and contains two subsets:\r\n\r\n- **ConsInv-Indoors** contains sequences in an office setting where small objects are moved.\r\n- **ConsInv-Outdoors** contains sequences in an urban environment, where cars and/or people move.\r\n\r\nThe novelty of ConsInv dataset is 1) the controlled degree of difficulty, from easy to very hard, and 2) the fact that the difficulty of the sequences comes only from object motion: relative motion between camera and object, motion ambiguity, challenging points of view when objects move. The difficulty does not come from motion speed, lack of features, lens flare, etc. - typically seen in other SLAM datasets.","description_withheld":null,"homepage":"https://github.com/adrianbojko/consinv-dataset","introduced_date":"2022-10-15","introduced_date_note":null,"introduced_by":{"paper":"/paper/self-improving-slam-in-dynamic-environments","title":"Self-Improving SLAM in Dynamic Environments: Learning When to Mask","first_author":"Adrian Bojko","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Stereo","url":"/datasets/modality/stereo"},{"name":"RGB Video","url":"/datasets/modality/rgb-video"}],"tasks":[{"name":"Visual Odometry","url":"/task/visual-odometry","datasets_with_task":"/datasets/task/visual-odometry"},{"name":"Semantic SLAM","url":"/task/semantic-slam","datasets_with_task":"/datasets/task/semantic-slam"},{"name":"Monocular Visual Odometry","url":"/task/monocular-visual-odometry","datasets_with_task":"/datasets/task/monocular-visual-odometry"},{"name":"Object SLAM","url":"/task/object-slam","datasets_with_task":"/datasets/task/object-slam"}],"languages":[],"variants":["ConsInv 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."}