lidar_slam_ros2
Outdoor walk, APE 0.55 m (Koide, CC BY 4.0) · camera-coloured park (RTK-SLAM, CC BY 4.0) · stairs, ATE 0.24 m on development data · held-out metro tunnel, 0.38 m like BIEVR-LIO and FAST-LIO2 (GEODE) · three separate walks joined into one map with no position hint (Oxford Spires, CC BY-NC-SA 4.0). How these were made.
Turn a rosbag into a map you can actually drive on. ROS 2 LiDAR SLAM that writes an Autoware-compatible map bundle: pointcloud_map/, map_projector_info.yaml and lanelet2. RKO-LIO (MIT) + graph_based_slam (BSD-2), no GPL on the default workflow. 日本語クイックスタート · release notes v0.10.0 · next v0.10.1
Quickstart
Choose your shortest path
| Goal | Start here | Safety and cost boundary |
|---|
| See a verified map, no build | Default if unsure: Docker demo | Stable v0.10.0-humble; needs Docker; host writes stay in ./lidarslam_output. |
| Map my own rosbag | Own-bag route: lidarslam-map doctor /path/to/rosbag2 | Read-only diagnosis first; then lidarslam-map start /path/to/rosbag2 writes a new output. |
| Build from source or contribute | Source quickstart: bash scripts/source_quickstart.sh --dry-run | Release v0.10.0; needs ROS 2, 8 GiB, and roughly 30 minutes. |
Try it with Docker (one command, no build)
docker run --rm -e LIDARSLAM_HOST_UID="$(id -u)" -e LIDARSLAM_HOST_GID="$(id -g)" \
-v "$PWD/lidarslam_output:/lidarslam_ws/output" \
ghcr.io/rsasaki0109/lidar_slam_ros2:v0.10.0-humble
The 517 MB MID-360 demo writes lidarslam_output/mid360_demo/ as your user. Other platforms: Getting Started.
Map your own bag (one command after install)
lidarslam-map start /path/to/rosbag2
Run lidarslam-map with no arguments for a guided start, or lidarslam-map doctor first: it uses no network and writes no files. See supported inputs.
Build + verified demo from source (one helper)
mkdir -p ~/ros2_ws/src
cd ~/ros2_ws/src
git clone --recursive https://github.com/rsasaki0109/lidar_slam_ros2.git
cd lidar_slam_ros2
bash scripts/source_quickstart.sh
The helper detects Humble/Jazzy, installs the dependencies, builds the six maintained packages and runs the verified demo (--dry-run, --build-only). It prints a lidarslam-map path that auto-activates this build. Needs ROS 2, 8 GiB and about 30 minutes. Before handoff: python3 scripts/check_first_map_verification_package.py --json, continue only on READY.
Use your own bag
For an Ouster, Velodyne, RoboSense or other PointCloud2 bag, do not edit this package's launch files or YAML first: run lidarslam-map doctor, then lidarslam-map start. It checks topics, frames, fields, timestamps, a maintained profile and calibration, and stops unsafe inputs with a stable reason code and one next action. Maintained inputs: PointCloud2+Imu, PointCloud2+NavSatFix, VelodyneScan+Applanix GSOF49.
- Docker, no ROS:
bash scripts/docker_map_bag.sh /absolute/path/to/rosbag2 mounts the bag read-only (Docker Own-Bag Map).
- After a run:
lidarslam-map view, lidarslam-map sessions, lidarslam-map compare day1 day2, lidarslam-map support day1, lidarslam-map merge day1 day2 --output-dir site_project.
- Report on a fixed output:
lidarslam-map report /path/to/output/mid360_demo --json (first-map validation guide).
- Automation:
lidarslam-map run (Operator workflows). Localization: Localize On The Map.
Why lidarslam_ros2
- Autoware-compatible output:
verify_autoware_map.py prints map_verify: PASS on every saved bundle.
- lanelet2 auto-generation from the SLAM trajectory, validated for Autoware routing.
- Surveyed ground truth: per-dataset APE thresholds gate every release in CI (accuracy).
- Loop closure, GPL-free: Scan Context, BEV / SOLiD / Triangle descriptors and 3D-BBS verification (opt-in).
- Tunnels and fog: radar fusion maps a ~500 m tunnel; without radar, an Ouster preset holds all ten ENWIDE sequences; on GEODE's 13 held-out tunnel bags, no failure but not the most accurate (comparison, guide).
- Deterministic offline mapping: byte-identical trajectories, loop edges and submaps.
- Refined maps: plane bundle adjustment under holdout-validated thresholds (evidence).
flowchart LR
bag(["rosbag2"]) --> rko["RKO-LIO<br/>LiDAR-inertial odometry"]
rko --> gbs["graph_based_slam<br/>loop closure + graph optimization"]
gbs --> bundle["Autoware map bundle<br/>pointcloud_map · lanelet2 · projector info"]
Camera-coloured point-cloud maps
The pipeline refines each camera pose against the LiDAR map, then projects the pixels onto it. On the park walk at the top (RTK-SLAM Stadtgarten 2; MP4 · GIF) the refinement cuts the held-out epipolar error from 4.02 to 0.67 px (tool, release reference).
python3 tools/colored_map/colored_map_pipeline.py \
<bag> output/<run>/traj_corrected.tum output/<run>/colored_map \
--raw-traj output/<run>/traj_raw.tum \
--extrinsic configs/gaussian_splatting/<lidar_camera_extrinsic>.yaml
Moving rigs: --refine-spatiotemporal-calibration (design). Regression gates: Benchmarking.
Open-source benchmark results
On the same HILTI 2022 exp04 LiDAR/IMU input and CPU-only host, the competitive
profile recorded 44.1% lower median APE RMSE than GLIM over three runs:
| System | Median APE RMSE | Median processing RTF | Maximum peak RSS |
|---|
| lidarslam_ros2 | 0.0482 m | 0.194 | 369.4 MB |
| GLIM CPU | 0.0863 m | 0.218 | 696.0 MB |
This is a scoped HILTI exp04 result from one 4-vCPU cloud VM (2026-10-19, three alternated runs per system, GLIM v1.2.2 built natively because the VM has no Docker daemon). There, lidarslam_ros2 has the lower median APE, processing RTF and peak RSS; the RTF ranges barely separate. It is not an overall best-system claim. Commands, scoring rules, the earlier Docker-based record and map-quality limits: Comparison.
The separate Voxel-SLAM v17 research candidate also achieved the lowest
geometric-mean APE across NavINST, Oxford, and UrbanNav: 2.2836 m, versus
GLIM 5.0779, Point-LIO 3.8388, FAST-LIO2 6.9576, and fixed Voxel-SLAM
2.7160 — 55.0% lower than GLIM. It is not the default release path, does
not win every sequence, and is not fresh-blind evidence. Exact revisions,
per-sequence results, input hashes, resource results, map limitations, and
reproduction notes are in Comparison.
Development-data results with loop closure on the MapsHD humanoid MID-360
trials, Oxford Spires Keble 05 and HILTI exp04, with settings chosen on those
same sequences, are in
Comparison.
Accuracy
Release-gate thresholds (Benchmarking) block every release in CI. Tunnel and fog mapping: Degeneracy Resilience Guide.
Docs
Preview the doc site locally: python3 -m mkdocs serve.
Support and license
| ROS 2 distro | Ubuntu | Scope |
|---|
| Humble | 22.04 | default workflow build + package tests in CI |
| Jazzy | 24.04 | default workflow build + package tests in CI; Autoware dogfood exercised locally |
graph_based_slam is BSD-2-Clause; RKO-LIO, DLIO, and the optional vendored
3D-BBS are MIT; FAST_GICP is BSD-3-Clause; built-in Scan Context is local. GPL-only
components (Thirdparty/lio-sam, Thirdparty/3d_bbs) are excluded via COLCON_IGNORE.
Quality gates
bash scripts/run_default_ci_checks.sh
Full gate list and parameter pointers: docs/workflows.md.
If this project saves you mapping time, a ⭐ helps others find it.
