Environment-Adaptive Solid-State LiDAR-Inertial Odometry
Zhi Zhang, Chalermchon Satirapod, Bingtao Ma, Changjun Gu
TLDR
This paper proposes an environment-adaptive LiDAR-inertial odometry using normal-vector constraints and degeneracy-aware map maintenance for robust localization in extreme environments.
Key contributions
- Introduces local normal-vector constraints to stabilize state estimation and suppress localization drift.
- Designs a degeneration-guided map update strategy to improve map precision and consistency.
- Enhances localization accuracy and robustness in extreme, perceptually degraded environments.
- Achieves up to 12.8% RMSE reduction compared to baseline methods.
Why it matters
Accurate LiDAR-inertial SLAM in extreme environments is crucial but challenging due to geometric degeneracy. This paper provides a robust solution by adaptively handling these issues, significantly improving mapping accuracy and reliability. This advancement is vital for autonomous systems operating in complex, real-world conditions.
Original Abstract
Solid-state LiDAR-inertial SLAM has attracted significant attention due to its advantages in speed and robustness. However, achieving accurate mapping in extreme environments remains challenging due to severe geometric degeneracy and unreliable observations, which often lead to ill-conditioned optimization and map inconsistencies. To address these challenges, we propose an environment-adaptive solid-state LiDAR-inertial odometry that integrates local normal-vector constraints with degeneracy-aware map maintenance to enhance localization accuracy. Specifically, we introduce local normal-vector constraints to improve the stability of state estimation, effectively suppressing localization drift in degenerate scenarios. Furthermore, we design a degeneration-guided map update strategy to improve map precision. Benefiting from the refined map representation, localization accuracy is further enhanced in subsequent estimation. Experimental results demonstrate that the proposed method achieves superior mapping accuracy and robustness in extreme and perceptually degraded environments, with an average RMSE reduction of up to 12.8% compared to the baseline method.
📬 Weekly AI Paper Digest
Get the top 10 AI/ML arXiv papers from the week — summarized, scored, and delivered to your inbox every Monday.