📍PIN-SLAM: LiDAR SLAM Using a Point-Based Implicit Neural Representation for Achieving Global Map Consistency
Yue Pan · Xingguang Zhong · Louis Wiesmann . Thorbjörn Posewsky . Jens Behley · Cyrill Stachniss
TL;DR: PIN-SLAM is a full-fledged implicit neural LiDAR SLAM system including odometry, loop closure detection, and globally consistent mapping
Globally consistent point-based implicit neural (PIN) map built with PIN-SLAM in Bonn. The high-fidelity mesh can be reconstructed from the neural point map.
Comparison of (a) the inconsistent mesh with duplicated structures reconstructed by PIN LiDAR odometry, and (b) the globally consistent mesh reconstructed by PIN-SLAM.
| Globally Consistent Mapping | Various Scenarios | RGB-D SLAM Extension |
|---|---|---|
demo_kitti00.mp4 |
demo_lidar_9scenes.mp4 |
demo_replica_rgbd.mp4 |
Table of Contents
[Details (click to expand)]
Accurate and robust localization and mapping are essential components for most autonomous robots. In this paper, we propose a SLAM system for building globally consistent maps, called PIN-SLAM, that is based on an elastic and compact point-based implicit neural map representation. Taking range measurements as input, our approach alternates between incremental learning of the local implicit signed distance field and the pose estimation given the current local map using a correspondence-free, point-to-implicit model registration. Our implicit map is based on sparse optimizable neural points, which are inherently elastic and deformable with the global pose adjustment when closing a loop. Loops are also detected using the neural point features. Extensive experiments validate that PIN-SLAM is robust to various environments and versatile to different range sensors such as LiDAR and RGB-D cameras. PIN-SLAM achieves pose estimation accuracy better or on par with the state-of-the-art LiDAR odometry or SLAM systems and outperforms the recent neural implicit SLAM approaches while maintaining a more consistent, and highly compact implicit map that can be reconstructed as accurate and complete meshes. Finally, thanks to the voxel hashing for efficient neural points indexing and the fast implicit map-based registration without closest point association, PIN-SLAM can run at the sensor frame rate on a moderate GPU.-
Ubuntu OS (tested on 20.04)
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With GPU (recommended) or CPU only (run much slower)
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GPU memory requirement (> 6 GB recommended)
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Windows/MacOS with CPU-only mode
conda create --name pin python=3.8
conda activate pin
conda install pytorch==2.0.0 torchvision==0.15.0 torchaudio==2.0.0 pytorch-cuda=11.7 -c pytorch -c nvidia
The commands depend on your CUDA version (check it by nvcc --version). You may check the instructions here.
pip3 install -r requirements.txt
git clone [email protected]:PRBonn/PIN_SLAM.git
cd PIN_SLAM
For a sanity test, do the following to download an example part (first 100 frames) of the KITTI dataset (seq 00):
sh ./scripts/download_kitti_example.sh
And then run:
python3 pin_slam.py ./config/lidar_slam/run_demo.yaml -vsm
[Details (click to expand)]
You can visualize the SLAM process in PIN-SLAM visualizer and check the results in the ./experiments folder.
Use run_demo_sem.yaml if you want to conduct metric-semantic SLAM using semantic segmentation labels:
python3 pin_slam.py ./config/lidar_slam/run_demo_sem.yaml -vsm
Use run_kitti_color.yaml if you want to test PIN-SLAM with the colorized point cloud using also the RGB images:
python3 pin_slam.py ./config/lidar_slam/run_kitti_color.yaml kitti 00 -i ./data/kitti_example -vsmd
If you are running on a server without an X service (you may first try export DISPLAY=:0), then you can turn off the visualization -v flag:
python3 pin_slam.py ./config/lidar_slam/run_demo.yaml -sm
If you don't have a Nvidia GPU on your device, then you can turn on the CPU-only operation by adding the -c flag:
python3 pin_slam.py ./config/lidar_slam/run_demo.yaml -vsmc
For an arbitrary data sequence, you can run with the default config file by:
python3 pin_slam.py -i /path/to/your/point/cloud/folder -vsm
[More Usage (click to expand)]
Follow the instructions on how to run PIN-SLAM by typing:
python3 pin_slam.py -h
To run PIN-SLAM with a specific config file, you can run:
python3 pin_slam.py path_to_your_config_file.yaml -vsm
The flags -v, -s, -m toggle the visualizer, map saving and mesh saving, respectively.
To specify the path to the input point cloud folder, you can either set pc_path in the config file or set -i INPUT_PATH upon running.
For pose estimation evaluation, you may also set pose_path in the config file to specify the path to the reference pose file (in KITTI or TUM format).
For some popular datasets, you can also set the dataset name and sequence name upon running. For example:
# KITTI dataset sequence 00
python3 pin_slam.py ./config/lidar_slam/run_kitti.yaml kitti 00 -vsm
# MulRAN dataset sequence KAIST01
python3 pin_slam.py ./config/lidar_slam/run_mulran.yaml mulran kaist01 -vsm
# Newer College dataset sequence 01_short
python3 pin_slam.py ./config/lidar_slam/run_ncd.yaml ncd 01 -vsm
# Replica dataset sequence room0
python3 pin_slam.py ./config/rgbd_slam/run_replica.yaml replica room0 -vsm
We also support loading data from rosbag, mcap or pcap (ros2) using specific data loaders (originally from KISS-ICP). You need to set the flag -d to use such data loaders. For example:
# Run on a rosbag or a folder of rosbags with certain point cloud topic
python3 pin_slam.py ./config/lidar_slam/run.yaml rosbag point_cloud_topic_name -i /path/to/the/rosbag -vsmd
# If there's only one topic for point cloud in the rosbag, you can omit it
python3 pin_slam.py ./config/lidar_slam/run.yaml rosbag -i /path/to/the/rosbag -vsmd
The data loaders for some specific datasets are also available. You need to set the flag -d to use such data loaders.
Available dataloaders: ['apollo', 'boreas', 'generic', 'helipr', 'kitti', 'kitti360', 'kitti_mot', 'kitti_raw', 'mcap', 'mulran', 'ncd', 'nclt', 'neuralrgbd', 'nuscenes', 'ouster', 'replica', 'rosbag', 'tum']
For example, you can run on Replica RGB-D dataset without preprocessing the data by:
# Download data
sh scripts/download_replica.sh
# Run PIN-SLAM
python3 pin_slam.py ./config/rgbd_slam/run_replica.yaml replica room0 -i data/Replica -vsmd
For example, you can run on KITTI-MOT dataset to test SLAM in dynamic scenes with online moving object segementation (MOS) by:
python pin_slam.py ./config/lidar_slam/run_kitti_mos.yaml kitti_mot 00 -i data/kitti_mot -vsmd --deskew
The SLAM results and logs will be output in the output_root folder set in the config file or specified by the -o OUTPUT_PATH flag.
For evaluation, you may check here for the results that can be obtained with this repository on a couple of popular datasets.
The training logs can be monitored via Weights & Bias online if you set the flag -w. If it's your first time using Weights & Bias, you will be requested to register and log in to your wandb account. You can also set the flag -l to turn on the log printing in the terminal and set the flag -r to turn on the visualization logging by rerun. If you want to get the dense merged point cloud map using the estimated poses of PIN-SLAM, you can set the flag -p.
If you are not using PIN-SLAM as a part of a ROS package, you can avoid the catkin stuff and simply run:
python3 pin_slam_ros.py path_to_your_config_file.yaml point_cloud_topic_name
[Details (click to expand)]
For example:
python3 pin_slam_ros.py ./config/lidar_slam/run.yaml /os_cloud_node/points
python3 pin_slam_ros.py ./config/lidar_slam/run.yaml /velodyne_points
After playing the ROS bag or launching the sensor you can then visualize the results in Rviz by:
rviz -d ./config/pin_slam_ros.rviz
You may use the ROS service save_results and save_mesh to save the results and mesh (at a default resolution) in the output_root folder.
rosservice call /pin_slam/save_results
rosservice call /pin_slam/save_mesh
The process will stop and the results and logs will be saved in the output_root folder if no new messages are received for more than 30 seconds.
If you are running without a powerful GPU, PIN-SLAM may not run at the sensor frame rate. You need to play the rosbag with a lower rate to run PIN-SLAM properly.
You can also put pin_slam_ros.py into a ROS package for rosrun or roslaunch.
We will add support for ROS2 in the near future.
After the SLAM process, you can reconstruct mesh from the PIN map within an arbitrary bounding box with an arbitrary resolution by running:
python3 vis_pin_map.py path/to/your/result/folder [marching_cubes_resolution_m] [(cropped)_map_file.ply] [output_mesh_file.ply] [mesh_min_nn]
[Details (click to expand)]
The bounding box of (cropped)_map_file.ply will be used as the bounding box for mesh reconstruction. This file should be stored in the map subfolder of the result folder. You may directly use the original neural_points.ply or crop the neural points in software such as CloudCompare. The argument mesh_min_nn controls the trade-off between completeness and accuracy. The smaller number (for example 6) will lead to a more complete mesh with more guessed artifacts. The larger number (for example 15) will lead to a less complete but more accurate mesh. The reconstructed mesh would be saved as output_mesh_file.ply in the mesh subfolder of the result folder.
For example, for the case of the sanity test described above, run:
python3 vis_pin_map.py ./experiments/sanity_test_* 0.2 neural_points.ply mesh_20cm.ply 8
Thanks @schneider-daniel for providing a docker container.
Build the docker container:
cd docker
sudo chmod +x ./build_docker.sh
./build_docker.sh
After building the container, configure the storage path in start_docker.sh and then run it by:
sudo chmod +x ./start_docker.sh
./start_docker.sh
We provide a PIN-SLAM visualizer based on lidar-visualizer to monitor the SLAM process. You can use -v flag to turn on it.
[Keyboard callbacks (click to expand)]
| Button | Function |
|---|---|
| Space | pause/resume |
| ESC/Q | exit |
| G | switch between the global/local map visualization |
| E | switch between the ego/map viewpoint |
| F | toggle on/off the current point cloud visualization |
| M | toggle on/off the mesh visualization |
| A | toggle on/off the current frame axis & sensor model visualization |
| P | toggle on/off the neural points map visualization |
| D | toggle on/off the training data pool visualization |
| I | toggle on/off the SDF horizontal slice visualization |
| T | toggle on/off PIN SLAM trajectory visualization |
| Y | toggle on/off the ground truth trajectory visualization |
| U | toggle on/off PIN odometry trajectory visualization |
| R | re-center the view point |
| Z | 3D screenshot, save the currently visualized entities in the log folder |
| B | toggle on/off back face rendering |
| W | toggle on/off mesh wireframe |
| Ctrl+9 | Set mesh color as normal direction |
| 5 | switch between point cloud for mapping and for registration (with point-wise weight) |
| 7 | switch between black and white background |
| / | switch among different neural point color mode, 0: geometric feature, 1: color feature, 2: timestamp, 3: stability, 4: random |
| < | decrease mesh nearest neighbor threshold (more complete and more artifacts) |
| > | increase mesh nearest neighbor threshold (less complete but more accurate) |
| [/] | decrease/increase mesh marching cubes voxel size |
| ↑/↓ | move up/down the horizontal SDF slice |
| +/- | increase/decrease point size |
If you use PIN-SLAM for any academic work, please cite our original paper.
@article{pan2024tro,
author = {Y. Pan and X. Zhong and L. Wiesmann and T. Posewsky and J. Behley and C. Stachniss},
title = {PIN-SLAM: LiDAR SLAM Using a Point-Based Implicit Neural Representation for Achieving Global Map Consistency},
journal = {IEEE Transactions on Robotics (TRO)},
volume = {40},
page = {4045-4064},
year = {2024},
codeurl = {https://github.com/PRBonn/PIN_SLAM}
}
If you have any questions, please contact:
- Yue Pan {[email protected]}
SHINE-Mapping (ICRA 23): Large-Scale 3D Mapping Using Sparse Hierarchical Implicit Neural Representations
LocNDF (RAL 23): Neural Distance Field Mapping for Robot Localization
KISS-ICP (RAL 23): A LiDAR odometry pipeline that just works
4DNDF (CVPR 24): 3D LiDAR Mapping in Dynamic Environments using a 4D Implicit Neural Representation