{"repo":"rsasaki0109/lidar_slam_ros2","free":true,"listed":false,"github":"https://github.com/rsasaki0109/lidar_slam_ros2","clone":"git clone https://github.com/rsasaki0109/lidar_slam_ros2.git","description":"ROS 2 LiDAR SLAM for pointcloud-map authoring, benchmarking, and Autoware-compatible map workflows.","language":"Python","stars":839,"topics":["autoware-compatible","benchmarking","gnss","lidar","lidar-slam","loop-closure","map-authoring","mapping","pointcloud-map","robotics","ros2"],"license":"BSD-2-Clause","category":"robotics_mapping","readme_excerpt":"# lidarslam_ros2\n\n[](https://github.com/rsasaki0109/lidar_slam_ros2/actions/workflows/main.yml)\n[](https://opensource.org/licenses/BSD-2-Clause)\n[](#support-and-license)\n[](https://github.com/rsasaki0109/lidar_slam_ros2/stargazers)\n\n**Turn a rosbag into a map you can actually drive on.**\n\nROS 2 LiDAR SLAM that outputs an Autoware-compatible map bundle — `pointcloud_map/`,\n`map_projector_info.yaml`, and auto-generated lanelet2. Frontend is `RKO-LIO` (MIT), backend is\n`graph_based_slam` (BSD-2). No GPL components on the default workflow.\n\n\n\n*Shinjuku point cloud map built from a demo rosbag with this stack — start at the\n[Quickstart](#quickstart). `develop` is the default branch; current release\ncandidate notes: [v0.9.0](docs/releases/v0.9.0.md).*\n\n## Why lidarslam_ros2\n\nMost LiDAR SLAM stacks stop at a trajectory and a point cloud. This one ships the\nartifacts you need downstream:\n\n- **Autoware-compatible output** — `pointcloud_map/` + `map_projector_info.yaml` open\n directly in Autoware map loaders; `verify_autoware_map.py` prints\n `map_verify: PASS` on every saved bundle.\n- **lanelet2 auto-generation** — drivable lanelets from the SLAM trajectory,\n validated for multi-segment Autoware routing.\n- **Surveyed ground truth** — releases are gated in CI by per-dataset APE\n thresholds, including total-station checkpoints on a Livox MID-360\n ([accuracy](#accuracy)).\n- **Loop closure, GPL-free** — opt-in built-in Scan Context, BEV / SOLiD /\n STD/BTC-style Triangle descriptors, and 3D-BBS verification.\n- **Tunnel / fog degeneracy presets** — opt-in radar fusion and gravity\n alignment map a ~500 m self-similar tunnel end-to-end\n ([result](#tunnel-and-fog-mapping-without-degeneracy-collapse), [guide](docs/degeneracy-guide.md)).\n- **Deterministic offline mapping** — `graph_slam_offline_runner` (backend,\n recorded odometry bag) and `scan_matcher_offline_runner` (frontend, raw bag)\n produce *byte-identical* trajectories, loop edges and submaps; the release\n gate enforces both.\n- **Globally refined, quality-gated maps** — clean-room plane bundle adjustment\n refines submap poses under holdout-validated quality thresholds\n ([evidence](docs/research/map-quality-baseline.md)).\n- **GNSS georeferencing** — optional GNSS constraints and projector metadata for\n real-world coordinates.\n- **Camera-coloured point-cloud maps** — synchronized images are projected onto\n registered LiDAR scans with calibration-aware, occlusion-resistant colouring.\n\n```mermaid\nflowchart LR\n bag([\"rosbag2\"]) --> rko[\"RKO-LIO
LiDAR-inertial odometry\"]\n rko --> gbs[\"graph_based_slam
loop closure + graph optimization\"]\n gbs --> bundle[\"Autoware map bundle
pointcloud_map · lanelet2 · projector info\"]\n```\n\n## Quickstart\n\nStart with [Getting Started](docs/getting-started.md). The [Product Contract](docs/product-contract.md)\ndefines the maintained surface; see [Distribution](docs/distribution.md) for installed names and binary-release limits.\n\n### Try it with Docker (one command, no build)\n\n```bash\nmkdir -p \"$PWD/lidarslam_output\"\ndocker run --rm -e LIDARSLAM_HOST_UID=\"$(id -u)\" -e LIDARSLAM_HOST_GID=\"$(id -g)\" \\\n -v \"$PWD/lidarslam_output:/lidarslam_ws/output\" \\\n ghcr.io/rsasaki0109/lidar_slam_ros2:humble\n```\n\nThis runs the 517 MB MID-360 demo with periodic progress and writes `lidarslam_output/mid360_demo/`.\nUID/GID returns it to the Linux user; see [Getting Started](docs/getting-started.md) for other platforms.\n\n### Build from source\n\n```bash\ncd ~/ros2_ws/src\ngit clone --recursive https://github.com/rsasaki0109/lidar_slam_ros2.git\ncd ..\nrosdep install --from-paths src --ignore-src -r -y\ncolcon build --symlink-install --cmake-args -DCMAKE_BUILD_TYPE=Release\nsource install/setup.bash\n```\n\nIf you cloned without `--recursive`: `git -C src/lidar_slam_ros2 submodule update --init --recursive`.\n\nThen run one public dataset end to end — NTU VIRAL `tnp_01` (~580 s outdoor bag)\nthrough RKO-LIO + graph_based_slam into an Autoware-loadable map:\n\n```bash\ncd src/lidar_slam_ros2\nbash scripts/download_ntu_viral_tnp01.sh\nbash scripts/run_autoware_quickstart.sh\npython3 scripts/verify_autoware_map.py output/.../pointcloud_map\n```\n\n## Use your own bag\n\n```bash\nlidarslam-map run /path/to/rosbag2 --output-dir \"$PWD/output/my_map\"\n```\n\nOne command turns the bag into a complete Autoware map bundle:\n`pointcloud_map/` tiles, `map_projector_info.yaml`, and a `lanelet2_map.osm`\ngenerated from the loop-closed trajectory.\n\nDirect launch commands, required topics, GNSS / IMU pre-integration, and\ndynamic-object filtering are in [docs/workflows.md](docs/workflows.md).\n\n\n\n## Camera-coloured point-cloud maps\n\nThe pipeline registers LiDAR scans with the corrected trajectory, then projects\nsynchronized camera pixels onto that geometry. This RTK-SLAM Construction Hall\n1 result follows the full estimated 60 m walking loop.\n\n ([MP4](lidarslam/images/map_flythrough_rtkslam.mp4) · [GIF](lidarslam/images/map_flythrough_rtkslam.gif))\n\nK4 has 4.91 M points, 76.66% colour coverage, and 11/11 profile checks passing. Pose-aware dynamic cleaning before K3's camera fusion improves held-out RGB median from 41.17 to 40.54 and planar roughness median from 7.23 to 6.40. See the [release-readiness record](docs/research/colored-map-release-readiness-2026-07.md) for paired K3/K4 evidence and limits.\nThe sequence is from RTK-SLAM (CC-BY 4.0); its total-station checkpoints also drive the [accuracy gate](#accuracy).\n\nIf graph optimization outputs sparse keyframes, the coloured-map pipeline can\npropagate their corrections onto the dense SLAM pose stream automatically:\n\n```bash\npython3 tools/colored_map/colored_map_pipeline.py \\\n output//traj_corrected.tum output//colored_map \\\n --raw-traj output//traj_raw.tum \\\n --extrinsic configs/gaussian_splatting/.yaml\n```\n\nThe pipeline caches `dense_corrected_trajectory.tum` and rebuilds stale downstream artifacts; use `--force-trajectory` for an explicit refresh.\nMoving rigs can add `--refine-spatiotemporal-calibration`; see the [held-out-gated design and RTK-SLAM result](docs/research/colored-map-spatiotemporal-calibration-2026-07.md).\n\n### Cross-repository SLAM benchmark\n\n`public_suite_v1.yaml` connects Localization Zoo trajectories to trajectory,\ngeometry, real-RGB, runtime, and memory gates, with frozen OFF/ON candidate\npromotion across MID-360, HILTI, and RTK-SLAM surveyed references. Commands,\nreplay, and adoption records:\n[Benchmarking and release gate](docs/benchmarking.md#slam-candidate-regression).\n\n## Tunnel and fog mapping without degeneracy collapse\n\nOn the ~500 m self-similar Fyllingsdalen tunnel from the [NTNU LiDAR degeneracy datasets](https://github.com/ntnu-arl/lidar_degeneracy_datasets),\nthe plain frontend covers 98.7 m before along-axis degeneracy freezes it. The opt-in\npresets (radar ego-velocity fusion + sliding-window gravity alignment) map the whole\ntunnel — reach **504.5 m**, transverse RMS 1.34 m, end-height −4.7 m (−33 m without\ngravity alignment) — and cut fog clutter-lock drift 35.6 → 9.6 m, with defaults\nunchanged (a MID-360 driving holdout stays byte-identical). Symptom table:\n[Degeneracy Resilience Guide](docs/degeneracy-guide.md); evidence: [research note](docs/research/gravity-window-alignment-2026-07.md).\n\n\n\n## Accuracy\n\nCurrent numbers from the release-gate profiles (`scripts/release_profiles.yaml`).\nThe pre-release gate rejects missing evidence and regressions in every blocking profile.\n\n| Dataset | Sensor | Reference | APE RMSE | Gate (pass) |\n| --- | --- | --- | --- | --- |\n| NTU VIRAL `tnp_01` (outdoor, ~580 s) | Ouster OS1-16 + VN-100 | Leica prism ground truth | **0.95 m** (best 0.87) | ≤ 1.00 m |\n| RTK-SLAM Construction Hall 2 (indoor, ~600 s) | Livox MID-360 | total-station checkpoints¹ | **0.086 m** (median 0.064, 16/16) | ≤ 0.30 m |\n| RTK-SLAM Construction Hall 1 (indoor, ~741 s) | Livox MID-360 | total-station checkpoints¹ | **0.321 m** (median 0.163, 16/16) | ≤ 0.55 m |\n| RTK-SLAM Stadtgarten 2 (outdoor park, ~876 s) | Livox MID-360 | total-station checkpoints¹ | **0.426 m** (median 0.264, 19/19) | report-only² |\n| RTK-SLAM Stadtgarten 1 (outdoor park, ~1 km loop) | Livox MID-360 | total-station checkpoints¹ | **0.838 m** (median 0.511, 36/36) | report-only² |\n| Newer College `Maths-Hard` (~320 m loop) | Ouster OS0-128 | ICP registration to survey-grade map | candidate evidence required locally | ≤ 0.10 m |\n\n¹ Surveyed checkpoints from the public RTK-SLAM dataset (CC-BY 4.0), scored like\nits published baselines (dense odometry trajectory).\n² Outdoor profiles soak as report-only before graduating; the former GLIM\ncross-validation gate is also report-only since v0.5. Methodology and\ncaveats: [docs/comparison.md](docs/comparison.md).\n\nReproduce locally:\n```bash\npython3 scripts/download_rtk_slam_dataset.py --sequence construction_seq2 --eval-assets\npython3 scripts/run_rtk_slam_accuracy_suite.py\n```\n\nDetails and optional MID-360 / production-bundle gates: [docs/benchmarking.md](docs/benchmarking.md).\n\n## Degeneracy resilience (fog / tunnels)\n\nOpt-in radar/intensity gates recover fog \"clutter-lock\" (drift **35.6 → 11.2 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