[IROS 2026] Chain-SLAM: Globally Consistent Backend for Multi-Session LiDAR SLAM via Chained Loop Closure

Zhiheng Li, Xinhao Liu, Juexiao Zhang, Yongqing Liang, Chen Feng

New York University

TL;DR: Chain-SLAM is a real-time LiDAR–inertial SLAM backend for online multi-session map alignment and reuse, propagating loop-closure constraints across sessions via chained loop closure for globally consistent large-scale mapping.

Evaluated on the multi-session MARS and NCLT datasets · pre-converted ROS bags here.

Direct and chained loop closures between a current session i and loaded sessions j, k. Each successful closure from the current session triggers multiple chained closures to the loaded sessions, enhancing multi-session consistency.

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Results

Estimated trajectories across multiple sessions (top vs. bottom rows) overlaid per session, with the per-session alignment error against ground truth shown on the right.

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Getting Started

1. Build the Docker environment

The docker/ directory provides a reproducible ROS Noetic environment. The image bakes in the system dependencies and GTSAM; the workspace and Open3D are built once inside the container. See docker/README.md for details.

# On the host, from the repo root:

# 1. Build the image (apt deps + GTSAM from source)
docker build -t chainslam_ros1:latest docker/

# 2. Create & enter the container (re-run this later to re-enter it)
./docker/run.sh

# --- inside the container ---

# 3. Build Open3D 0.15.1 from source (once)
./docker/install_open3d.sh

# 4. Build the catkin workspace (clones livox_ros_driver, then catkin_make)
./docker/build_ws.sh

# 5. Source the overlay
source devel/setup.bash

The repo is bind-mounted at the same path inside the container as on the host, so launch-file and pipeline paths resolve identically. run.sh enables GUI (rviz) access, optional NVIDIA GPU passthrough, and mounts /home, /mnt, and /media for rosbags and outputs.

After code changes, re-run ./docker/build_ws.sh (or catkin_make) inside the container and re-source devel/setup.bash.

If a crash fills the host disk with core dumps: sudo rm -rf /var/lib/apport/coredump/*

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2. Run the mapping pipeline

The pipeline consumes one ROS bag per scene (Velodyne-format point clouds, plus IMU/GPS topics). Prepare these bags from your dataset beforehand.

Mapping uses three terminals, each running a shell inside the same container. The key idea is chaining: each scene loads the map produced by the previous scene(s), so poses are optimized against the accumulated map. To map a scene standalone, skip the /load_map step (Terminal 2).

Entering the container (per terminal)

./docker/run.sh enables GUI (rviz) access on the host and docker execs a new shell into the already-running chainslam_ros1 container — so run it once in each of the three host terminals. Inside every shell, source ROS and the workspace overlay before running ROS commands:

# On the host, from the repo root, in a fresh terminal:
./docker/run.sh

# --- now inside the container ---
source /opt/ros/noetic/setup.bash
source devel/setup.bash

If rviz fails to open with a display error, run xhost +local:docker on the host once, then re-enter the container. (run.sh already attempts this.)

Terminal 1 — launch mapping (opens rviz)

log_subfolder names the output folder under Log/. mode selects the config (full_raw | optimize_pose | optimize_z).

./docker/run.sh                            # enter the container
source /opt/ros/noetic/setup.bash
source devel/setup.bash
roslaunch fast_lio_sam mapping_velodyne128_mars.launch log_subfolder:="scene_0"

Terminal 2 — (optional) load a prior map to chain onto

./docker/run.sh                            # enter the container
source /opt/ros/noetic/setup.bash
source devel/setup.bash
rosservice call /load_map "source: '/Documents/.../output/scene_prev'"

Terminal 3 — replay the bag

./docker/run.sh                            # enter the container
source /opt/ros/noetic/setup.bash
source devel/setup.bash
rosbag play scene_0.bag

After playback finishes

In any container shell, save the optimized pose and map (create the destination directory first — the service does not create it):

mkdir -p /Documents/.../output/scene_0

rosservice call /save_pose "resolution: 0.0
destination: '/Documents/.../output/scene_0'"

rosservice call /save_map "resolution: 0.0
destination: '/Documents/.../output/scene_0'"

Chaining multiple scenes

To build a single map across scenes 0→1→2→3→4, run the loop above per scene, loading the previous scene's output as the map and saving to a cumulative folder:

Scene	log_subfolder / save dir	/load_map source
0	scene_0	(none)
1	scene_0+1	scene_0
2	scene_0+1+2	scene_0+1
3	scene_0+1+2+3	scene_0+1+2
4	scene_0+1+2+3+4	scene_0+1+2+3

Each scene's output directory holds the optimized poses (optimized_pose.txt), the keyframe map, and transformations.pcd for downstream evaluation.