DALI-SLAM: Degeneracy-Aware LiDAR-inertial SLAM with novel distortion correction and accurate multi-constraint pose graph optimization

The code will be open-sourced and refined after acceptance.

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

🔭 Introduction

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.

🔗 Related Works

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

🔗 Competition

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.

💡 Citation

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}
}

🔗 Acknowledgments

We sincerely thank the excellent projects:

• FAST-LIO2
• basalt-headers
• GTSAM
• Ceres
• Sopuhs
• PCL