Dynamic-LIVO: A Dynamic-Aware LiDAR-Inertial-Visual Odometry System Using Spatio-Temporal Normals

Abstract:This paper proposes Dynamic-LIVO, a dynamic-aware LiDAR-Inertial-Visual Odometry (LIVO) system for robust state estimation and static colored mapping in dynamic environments. Dynamic-LIVO employs Spatio-Temporal (S-T) normal analysis to identify dynamic LiDAR points and propagates the resulting classification to both LiDAR-inertial and visual-inertial updates, preventing dynamic LiDAR measurements and their associated visual observations from affecting state estimation and mapping. However, S-T normal estimation can be unreliable in newly observed and spatially sparse regions due to insufficient spatio-temporal observations. To address this issue, we introduce a time-delayed S-T normal estimation strategy that defers the classification of insufficiently constrained points and re-evaluates them as additional observations become available. This strategy improves dynamic classification reliability while preserving valid static points for map construction. Extensive experiments on public and self-collected datasets with diverse sensor configurations demonstrate that Dynamic-LIVO improves localization accuracy and produces cleaner static colored maps in challenging dynamic environments. The source code and self-collected dataset will be publicly released upon acceptance.

Robotics (cs.RO)

arXiv:2609.19336 [cs.RO]

(or arXiv:2609.19336v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2609.19336 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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