lidarslam_ros2

ROS 2 LiDAR SLAM focused on non-GPL pointcloud-map authoring, benchmarking, and compatibility with Autoware pointcloud-map workflows.

Status: develop tracks the current v2 alpha line. For the latest tagged public beta, see v0.2.2 Release Notes.

Recommended Public Workflow

The recommended public path in this repository is:

• frontend: RKO-LIO
• backend: graph_based_slam
• output: Autoware-compatible pointcloud_map/ and map_projector_info.yaml

This is the path exercised in the public quickstart, benchmark flow, and release/readiness gate.

Scope

This repository is for people who want:

• ROS 2 LiDAR SLAM with loop closure
• pointcloud maps that Autoware can load
• lanelet2 maps auto-generated from SLAM trajectories
• end-to-end autonomous driving on self-made maps (AWSIM + Autoware)
• a non-GPL default workflow

Out of scope for the public path:

• Autoware planning/localization bringup beyond the provided demo scripts
• GPL-only frontend or backend components in the default workflow

Why This Repo

• non-GPL default path: graph_based_slam, scanmatcher, RKO-LIO, DLIO, and optional FAST_GICP
• pointcloud-map authoring is treated as a first-class workflow, not just a side-effect of odometry
• Autoware pointcloud-map flow is exercised end-to-end
• AWSIM → lidarslam → Autoware autonomous driving pipeline with one-command demo
• lanelet2 auto-generation from SLAM trajectories (multi-segment with shared boundary nodes)
• default benchmark path is tracked on NTU VIRAL
• current long-loop evidence is tracked on MID360
• optional GNSS georeferencing writes map_projector_info.yaml; GNSS edges can use covariance-based weighting
• GPL-free Scan Context place recognition is available in graph_based_slam
• optional MIT-licensed 3D-BBS loop-candidate verification can be built from the vendored Thirdparty/3d_bbs source and remains disabled at runtime by default
• report helpers cover benchmarks, GNSS, cleanup, dynamic filtering, place recognition, and submission bundles

Install

Clone with submodules and install dependencies:

cd ~/ros2_ws/src
git clone --recursive https://github.com/rsasaki0109/lidarslam_ros2.git
cd ..
rosdep install --from-paths src --ignore-src -r -y

If you already cloned without submodules:

git -C src/lidarslam_ros2 submodule update --init --recursive

Build the workspace:

colcon build --symlink-install --cmake-args -DCMAKE_BUILD_TYPE=Release

Quickstart

# local checks
bash scripts/run_default_ci_checks.sh

# fixed public quickstart
bash scripts/download_ntu_viral_tnp01.sh
bash scripts/run_autoware_quickstart.sh

# arbitrary rosbag2
bash scripts/run_autoware_map_beginner.sh /path/to/rosbag2

AWSIM Autonomous Driving Pipeline

AWSIM simulator data can flow through lidarslam into pointcloud_map, generated lanelet2, and Autoware driving demos. Start with:

bash scripts/test_awsim_setup.sh
bash scripts/run_awsim_selfmade_map_demo.sh

For map packaging, lanelet2 generation, and terminal-by-terminal bringup, see AWSIM Autonomous Driving Tutorial.

Point-Cloud Map Example

Autoware-compatible browser proof built from a live /map/pointcloud_map: the rendered map comes from Autoware map loaders, map verify is PASS, and GNSS runs emit LocalCartesian. Save-time dynamic-object filtering on Leo Drive bag6 cuts saved points by about 50% while keeping verification PASS.

Docs

• AWSIM Autonomous Driving Tutorial
• Autoware-Compatible Map Authoring / Autoware Quickstart / Autoware Foxglove
• Operator Workflows
• Benchmarking And Release Gate
• Comparison
• v0.2.2 Release Notes
• Contributing
• Changelog
• Releasing

Preview the doc site locally with python3 -m mkdocs serve.

Current Snapshot

The current public evidence covers NTU VIRAL, MID360, GNSS map metadata, and dynamic-filter save-time cleanup. More detail lives in docs/comparison.md, docs/benchmarking.md, output/benchmark_summary.md, and output/latest_report.html.

Main Entrypoints

Required input topics for the main public path:

Launch path	Required topics	Optional topics
ros2 launch lidarslam rko_lio_slam.launch.py	LiDAR sensor_msgs/PointCloud2 on lidar_topic, IMU sensor_msgs/Imu on imu_topic	sensor_msgs/NavSatFix on gnss_topic (default: /gnss/fix) when use_gnss:=true
ros2 launch lidarslam lidarslam.launch.py	Point cloud sensor_msgs/PointCloud2 on input_cloud, TF from robot_frame_id to the LiDAR frame	IMU on imu_topic when scanmatcher use_imu:=true, odom TF when scanmatcher use_odom:=true, GNSS on gnss_topic (default: /gnss/fix) when backend use_gnss:=true
ros2 launch graph_based_slam graphbasedslam.launch.py	lidarslam_msgs/MapArray on map_array	IMU on /imu when use_imu_preintegration:=true, GNSS on gnss_topic (default: /gnss/fix) when use_gnss:=true

There is no wheel-speed / vehicle-speed input in the current public path yet. The backend GNSS subscription topic is configurable with gnss_topic (default: /gnss/fix). Inspect covariance with scripts/inspect_navsatfix_covariance.py; Applanix conversion details live in docs/workflows.md.

Run the public Autoware quickstart:

bash scripts/run_autoware_quickstart.sh

Run RKO-LIO + graph_based_slam directly:

ros2 launch lidarslam rko_lio_slam.launch.py \
  bag_path:=/path/to/rosbag2 \
  lidar_topic:=/os_cloud_node/points \
  imu_topic:=/os_cloud_node/imu

Save the current map:

ros2 service call /map_save std_srvs/srv/Empty

To filter likely dynamic objects only in the saved map output, enable use_dynamic_object_filter: true and tune dynamic_object_filter_voxel_size, dynamic_object_filter_min_observations, dynamic_object_filter_temporal_window, and dynamic_object_filter_max_range_from_sensor_m before calling /map_save.

Run the standard benchmark path:

bash scripts/download_ntu_viral_tnp01.sh
bash scripts/run_rko_lio_graph_benchmark.sh

For KITTI Odometry / Velodyne-only evaluation, Leo Drive classic-path suites, dynamic-filter benchmarks, and MID360 place-recognition comparisons, use the entrypoints documented in docs/benchmarking.md.

Run the local readiness gate:

bash scripts/run_release_readiness_checks.sh --ape-threshold 0.10

License Policy

The default public workflow excludes GPL-only frontend/backend components. graph_based_slam is BSD-2-Clause; RKO-LIO, DLIO, and optional vendored 3D-BBS are MIT; FAST_GICP is BSD-3-Clause; built-in Scan Context is implemented locally. Thirdparty/lio-sam and Thirdparty/3d_bbs are excluded from direct colcon package discovery via COLCON_IGNORE.

Support Matrix

ROS 2 distro	Ubuntu	Scope
Humble	22.04	default workflow build and package tests in CI
Jazzy	24.04	default workflow build and package tests in CI; Autoware pointcloud-map dogfood exercised locally

Quality Gates

The main checks for the public path are bash scripts/run_default_ci_checks.sh, python3 scripts/verify_autoware_map.py <pointcloud_map_dir>, bash scripts/run_autoware_quickstart.sh, bash scripts/run_rko_lio_graph_autoware_dogfood.sh --auto-exit-secs 20, and bash scripts/run_release_readiness_checks.sh --ape-threshold 0.10.

For the command-level details, parameter-file pointers, and Autoware map output notes, see docs/workflows.md.