DALI-SLAM: Degeneracy-Aware LiDAR-inertial SLAM with novel distortion correction and accurate multi-constraint pose graph optimization
DALI-SLAM: Degeneracy-Aware LiDAR-inertial SLAM with novel distortion correction and accurate multi-constraint pose graph optimization
Weitong Wu, Chi Chen, Bisheng Yang, Xianghong Zou, Fuxun Liang, Yuhang Xu, Xiufeng He
ISPRS Journal of Photogrammetry and Remote Sensing, 2025, 221: 92-108
Paper
Abstract: LiDAR-Inertial simultaneous localization and mapping (LI-SLAM) plays a crucial role in various applications such as robot localization and low-cost 3D mapping. However, factors including inaccurate motion distortion estimation and pose graph constraints, and frequent LiDAR feature degeneracy present significant challenges for existing LI-SLAM methods. To address these issues, we propose DALI-SLAM, an accurate and robust LI-SLAM that consists of degeneracy-aware LiDAR-inertial odometry (DA-LIO) with a dual spline-based motion distortion correction (DS-MDC) module, and multi-constraint pose graph optimization (MC-PGO). Considering the cumulative errors of micro-electromechanical systems (MEMS) inertial measurement unit (IMU) integration, two continuous-time trajectories in the sliding window are fitted to update the discrete IMU poses for accurate motion distortion correction. In the LiDAR-inertial fusion stage, LiDAR feature degeneracy is detected by analyzing the Jacobian matrix and a remapping strategy is introduced into the updating of error state Kalman Filter (ESKF) to mitigate the influence of degeneracy. Furthermore, in the back-end optimization stage, three types of submap constraints are accurately built with dedicated strategy through a robust variant of the iterative closest point (ICP) method. The proposed method is comprehensively validated using data collected from a helmet-based laser scanning system (HLS) in representative indoor and outdoor environments. Experiment results demonstrate that the proposed method outperforms the SOTA methods on the test data. Specifically, the proposed DS-MDC module reduces trajectory root mean square errors (RMSEs) by 7.9%, 5.8%, and 3.1%, while the degeneracy-aware update strategy achieves additional reductions of 43.3%, 17.7%, and 4.9%, respectively, across three typical sequences compared to existing methods, thereby effectively improving trajectory accuracy. Furthermore, the results of DA-LIO demonstrate an outdoor maximum drift accuracy of one thousandth of a meter, achieving superior performance compared to the SOTA method FAST-LIO2. After performing MC-PGO, the RMSEs of the trajectories are reduced by 25.2%, 9.2%, and 52.4%, respectively, across three typical sequences, demonstrating better performance compared to the SOTA method HBA.
Dataset:
WHU-Helmet Dataset: A helmet-based multi-sensor SLAM dataset for the evaluation of real-time 3D mapping in large-scale GNSS-denied environments
Calibration
AFLI-Calib: Robust LiDAR-IMU extrinsic self-calibration based on adaptive frame length LiDAR odometry
cd ~/catkin_ws/src
git clone https://github.com/DCSI2022/DALI_SLAM.git
cd DALI_SLAM
catkin_makeNeed solve the dependency before catkin_make, or use Docker
# in local
docker build -t $image_name:tag . #build custom name and tag from Dockerfile
docker run -it -v ~/catkin_ws/src/DALI-SLAM:/home/catkin_ws/src/DALI-SLAM -v $Data_folder_path:/home/data --network host --gpus all -u root $image_name:tag
# in container
cd /home/catkin_ws
catkin_make
source devel/setup.bash
Paramter description is provided in Parameter_Descrip. Check it!
we provide test data, you can download it and test it with the command below!
In local
roscore
rviz -d ~/catkin_ws/src/DALI-SLAM/DA_LIO/rviz_cfg/loam_livox.rviz
rosbag play test_mid70_zhuoer.bag
In container
roslaunch da_lio run_dalio.launch
In container
cd /home/catkin_ws
./devel/lib/backend_mapping/mc_pgo /home/data/test_data.bag src/dalislam/DA_LIO/Log/trajxxx.txt $path_to_extrinsic $lidar_type $lidar_topic $rosbag_start $rosbag_end $submap_length $overlap_threshold $IfSimulation
- Refactor MC-PGO in online mode
DALI-SLAM has been served as a system (partially modified) to participate in ICCV 2023 SLAM Challenge, achieving 3rd place on the LiDAR inertial track, 1st place in RPE metric, and 2nd place in ATE metric.
If you find this repo helpful, please give us a star . Please consider citing DALI-SLAM if this program benefits your project
@article{wu2025dali,
title={DALI-SLAM: Degeneracy-aware LiDAR-inertial SLAM with novel distortion correction and accurate multi-constraint pose graph optimization},
author={Wu, Weitong and Chen, Chi and Yang, Bisheng and Zou, Xianghong and Liang, Fuxun and Xu, Yuhang and He, Xiufeng},
journal={ISPRS Journal of Photogrammetry and Remote Sensing},
volume={221},
pages={92--108},
year={2025},
publisher={Elsevier}
}
We sincerely thank the excellent projects: