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Dual-Stage LiDAR-Inertial SLAM with Hierarchical Dynamic Object Removal in Dynamic Environments
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DOI：10.1007/s10514-026-10248-5.png)
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Abstract
En 中文
Dynamic content in traffic scenes can significantly affect LiDAR-Inertial Simultaneous Localization and Mapping (SLAM). Robust or maximum-likelihood formulations can attenuate inconsistent returns, but when motion is not explicitly modeled, challenges remain. Dynamic motion disrupts scan registration and mapping, leading to ghosting artifacts and a degradation in global map quality. To address this, we propose a dual-stage SLAM framework that constructs a visibility-consistent static subset. The first stage performs multi-resolution range-image differencing and applies visibility-aware incidence-angle correction to restore grazing-angle ground points that are often misclassified. The second stage conducts cluster-level reclassification to eliminate residual dynamics and recover static structures. The refined static points are fused within a tightly coupled factor graph using IMU pre-integration, scan-context loop closure, and a static-prior factor. Odometry features are visibility-gated and confidence-weighted to improve correspondence reliability in dynamic traffic conditions. Evaluations on the UrbanLoco and a self collected dynamic traffic dataset demonstrate up to 11.9% lower trajectory error and visibly cleaner static maps compared with state-of-the-art baselines. The results confirm that our method achieves more stable registration and long-term map fidelity in highly dynamic urban environments.
Keywords:
LiDAR-inertial SLAM
Dynamic environments
Visibility-aware filtering
Cluster reclassification
Factor-graph optimization
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