mirror of
https://github.com/introlab/rtabmap.git
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9c1e117384
* Sparse Bayes * updated perf test * improved tests with real data * Making sparse works in incremental mapping * bookkeeping optimization * small opt * refactoring * splitting dense and sparse in different classes to make the code more lisible * cleanup comments * fixing CI * Making all Bayes tests testing both dense and sparse * Added multisession_3it integration test (test memory management, multisession and dense/sparse bayes in that settings) * optimized sparse when transfer/retrieval happens (was slower than dense for that case) * Testing retrieval param variants * Updated multisession_3it integration tests to compare loop closure hypotheses * bump version * Fixed ui sum of prediction * adding g2o gtsam to linux ci * cleanup * added debug crash log for ci * Simplified Bayes/SparsePrediction description * Dont show too dense for sparse on small maps (e.g., when we just started a new map) * fixing amd64v3 issue with gtsam on ci ubuntu 26 * Dot not auto switch to dense based on map size. * updating test range * added coverage tests * Adressing coverage * ignore one line in coverage for purpose
33 lines
3.3 KiB
Plaintext
33 lines
3.3 KiB
Plaintext
# Test data assets fetched by scripts/fetch_test_data.sh.
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# Format: <basename>\t<source>\t<sha256>[\t<extracted>]
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# - basename is the file's name on disk under data/tests/
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# - source is one of:
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# * a Google Drive file ID (from https://drive.google.com/file/d/<ID>/...);
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# assembled into a direct-download URL by the fetch script.
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# * a full http(s):// URL; used as-is. Append "&resourcekey=<key>" to a
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# GDrive URL for legacy files (uploaded before 2017) that require it.
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# - sha256 must match the bytes of the file the tests read (compute with
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# `sha256sum file.db`), which for an archive is the file it holds rather than
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# the archive itself: a corrupt archive is caught by the extractor. Leave as
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# TODO_FILL_SHA256 to bypass the integrity check on first download then paste
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# the actual hash back in.
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# - extracted is optional and only for archives (.7z, .zip): the file the
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# archive holds, which the fetch script extracts into data/tests/ and which
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# the tests open. The archive is removed once extracted, so this file is both
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# what the sha256 is of and what tells the script the asset is already there.
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#
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# Lines starting with '#' and blank lines are ignored.
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netherdrone_lidar3d_sample_15s.db 1IBE8aMgY1_kmme_7Kb9W2wz6yBMLl63i 3fedb26d76d080e687d3926ab80cadee27d7c0115638562a75c1d9f08cc1c885
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pr2_scan2d_stereo_sample_15s.db 11GHZgwDzgC1Gv2hkmrPQQtP-dS9BGCA7 842fe941ce487b2109ae12ae796a9debf9e6a94df45cc2a0a9e4a47d41c5e878
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pr2_scan2d_rgbd_sample_15s.db 1vlFungKsdbnepgsuPHY-1h-mxdFxkZvI 1d8fd150a7f1d1e3dbb7cbb4f6ed66d0ac80cf06a6f5e859047e28bff2b96f14
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pr2_scan2d_corridor_50s.db 18ClEPTRM98icorrlDYBzKTWRwVOU8-bh f518cb4d9284b24a1500d3913cf67a098745229c12846b58520ec4cd085c2713
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2loops_workspace_3IT.db https://raw.githubusercontent.com/wiki/introlab/rtabmap/doc/Tutorials/Texture/2loops_workspace_3IT_updated.db 68f113fed7228d9081314f08b54240966c295cda0fb42046567edcf714445755
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robust_graph_optimization_stereo.db https://raw.githubusercontent.com/wiki/introlab/rtabmap/doc/Tutorials/RobustGraphOptimization/robust_graph_optimization_stereo.db 247694b5bdb82168ebe88bb6bb5bc31c7142234f8c64567afd93e464418cf02e
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loop_3it_gps.db https://raw.githubusercontent.com/wiki/introlab/rtabmap/doc/Tutorials/RobustGraphOptimization/loop_3it_gps.db 7aefeb573107a27b0a7826cb87ea03db495367aa56868b4d2b2543e6cacad3c8
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stereo_20Hz.db https://github.com/introlab/rtabmap/releases/download/0.23.1/stereo_20Hz.db 94e219e1c96e540cbb1490cc23c81c65b9e7b132e8d61eda05e7990bc13393b7
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multisession_3it.7z 10ulRDMqQy5V_3_kx_IeNzAJ8iUMdkkBZ 3d7b139e6b4c608185e44774288ce078b6364bfaeecd0c46c51007c95230c987 multisession_3it.db
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superpoint_v1.pth https://github.com/magicleap/SuperPointPretrainedNetwork/raw/refs/heads/master/superpoint_v1.pth 52b6708629640ca883673b5d5c097c4ddad37d8048b33f09c8ca0d69db12c40e
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superpoint_v6_from_tf.pth https://github.com/rpautrat/SuperPoint/raw/refs/heads/master/weights/superpoint_v6_from_tf.pth cd5d19a5061848e248c17728878ea166b66512076d43c77dbcf27f4a88a56084
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demo_superpoint.py https://raw.githubusercontent.com/magicleap/SuperPointPretrainedNetwork/master/demo_superpoint.py 613706ae7e9ce3fbc2cfe042fc3f37d739838cc2e1dc8ddd08f8ec037765df04
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superpoint_pytorch.py https://raw.githubusercontent.com/rpautrat/SuperPoint/master/superpoint_pytorch.py c06e3b0aad7548316ca59db3be9f56c7fcd126a1cf7a73359445fb778598f365
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