Hint

Please change branch to Bunker-DVI-Dataset-reg-1 for quick experiment.

Example Dataset:

Download the dataset from Bunker DVI Dataset

benchmark-DALI_SLAM-to-HDMapping

Runs the DALI_SLAM degeneracy-aware LiDAR-inertial odometry front-end (DA-LIO) on a ROS 1 bag file and converts the output to an HDMapping session.

DALI_SLAM is Degeneracy-Aware LiDAR-inertial SLAM with novel distortion correction and accurate multi-constraint pose graph optimization by Wu et al., ISPRS Journal of Photogrammetry and Remote Sensing, 2025 (paper). DA-LIO is FAST-LIO2-based.

Prerequisites

• Docker
• A ROS 1 bag containing a LiDAR topic and an IMU topic matching the topic names declared in the chosen DA-LIO config (ROS 2 bags are automatically converted to ROS 1 format). For the Livox profiles the LiDAR topic must be a livox_ros_driver/CustomMsg; the other profiles expect a sensor_msgs/PointCloud2.

Step 1 — Clone with submodules

git clone https://github.com/MapsHD/benchmark-DALI_SLAM-to-HDMapping.git --recursive
cd benchmark-DALI_SLAM-to-HDMapping

Step 2 — Build the Docker image

docker build -t dalislam_noetic .

This installs:

• Ubuntu 20.04 + ROS 1 Noetic
• Eigen3, PCL, OpenCV, Boost, OpenMP, TBB, glog/gflags
• GTSAM 4.0.3 and Ceres 2.1.0 (built from source at the prefixes DA-LIO expects)
• Livox-SDK + livox_ros_driver (provides CustomMsg)
• DA-LIO (compiled from the DALI_SLAM submodule; the MC-PGO back-end is skipped)
• catkin workspace with da_lio and dalislam_to_hdmapping

The build takes several minutes on first run (GTSAM and Ceres are built from source).

Step 3 — Run the pipeline

chmod +x docker_session_run-ros1-dalislam.sh
./docker_session_run-ros1-dalislam.sh /path/to/input.bag /path/to/output/dir

Or with no arguments to use a GUI file selector (requires zenity):

./docker_session_run-ros1-dalislam.sh

By default the script uses the helmet_mid profile (the upstream default config, which matches the Livox-Mid test bag). Pick a different one with the SENSOR environment variable, e.g.:

SENSOR=velodyne ./docker_session_run-ros1-dalislam.sh /path/to/input.bag /path/to/output/dir

Available sensor profiles (from the DALI_SLAM/DA_LIO/config/ directory):

SENSOR	Config file	LiDAR type	Config LiDAR topic	Config IMU topic
helmet_mid	helmet_mid.yaml	Livox (Mid)	/livox/lidar	/imu0
helmet_avia	helmet_avia.yaml	Livox (Avia)	/livox/lidar	/imu0
avia	avia.yaml	Livox (Avia)	/livox/lidar	/livox/imu
horizon	horizon.yaml	Livox (Horizon)	/livox/lidar	/livox/imu
hesai	hesai.yaml	Hesai	/hesai/pandar	/alphasense/imu
ouster64	ouster64.yaml	Ouster OS	/os_cloud_node/points	/os_cloud_node/imu
velodyne	velodyne.yaml	Velodyne	/velodyne_points	/imu/data

If your bag uses different topic names than the profile's config, remap them on playback with LIDAR_TOPIC / IMU_TOPIC (the value is the name in the bag):

SENSOR=velodyne LIDAR_TOPIC=/points_raw IMU_TOPIC=/imu/data_raw \
  ./docker_session_run-ros1-dalislam.sh /path/to/input.bag /path/to/output/dir

What happens:

The script opens a Docker container with a tmux session containing five panes on window 0 and a control window (window 1, the attach target):

Pane	Role
0	roscore
1	roslaunch da_lio run_dalio_bench.launch — subscribes to the LiDAR + IMU topics, publishes /Odometry + /cloud_registered (+ RViz live view)
2	rosbag record /Odometry /cloud_registered — captures the odometry and the registered world cloud
3	rosbag play --clock — plays your input bag with simulated clock
4	diagnostics — shows active topics and publishing rates

Press Ctrl+b then 0 to switch to window 0 and watch RViz. When playback finishes, the control window stops the recorder, kills all nodes and RViz, and exits tmux. A second Docker run then converts the recorded bag into the HDMapping session format.

Step 4 — Open in HDMapping

Output files appear in <output_dir>/output_hdmapping-DALI_SLAM/:

lio_initial_poses.reg
poses.reg
scan_lio_0.laz
scan_lio_1.laz
...
session.json
trajectory_lio_0.csv
trajectory_lio_1.csv
...

Open session.json with the multi_view_tls_registration_step_2 application.

Notes on DA-LIO

DA-LIO (like FAST-LIO2) publishes:

Topic	Type	Meaning
/Odometry	nav_msgs/Odometry	the current 6-DoF body pose in the global frame
/cloud_registered	sensor_msgs/PointCloud2	the current scan, already registered into the global frame

Because /cloud_registered is already in the world frame, the converter does not re-apply the pose to the points — it only uses /Odometry to build the per-chunk trajectory files. The recorded topics are tunable via env vars:

Variable	Meaning	Default
ODOM_TOPIC	DA-LIO odometry output	/Odometry
CLOUD_TOPIC	DA-LIO registered cloud (world)	/cloud_registered

This benchmark captures DA-LIO's online odometry output, consistent with the other LIO benchmarks in this repo. DALI_SLAM's MC-PGO back-end (offline multi-constraint pose graph optimization) is a separate stage and is not part of the recorded session.

Contact

januszbedkowski@gmail.com