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.
Keywords
Subject
Publication details
- Journal
- Not available
- Open access
- Green open access
Cite this article
APA 7
Zhang, Z., Ahiwe, S., Hale, M., Zhao, L., & Ladosz, P. (2026). Dynamic-LIVO: A Dynamic-Aware LiDAR-Inertial-Visual Odometry System Using Spatio-Temporal Normals. https://omanscience.com/en/articles/dynamic-livo-a-dynamic-aware-lidar-inertial-visual-odometry-system-using-spatio-temporal-normals
MLA 9
Zhang, Zhixin, et al. "Dynamic-LIVO: A Dynamic-Aware LiDAR-Inertial-Visual Odometry System Using Spatio-Temporal Normals." https://omanscience.com/en/articles/dynamic-livo-a-dynamic-aware-lidar-inertial-visual-odometry-system-using-spatio-temporal-normals.
Chicago (author–date)
Zhang, Zhixin, Samuel Ahiwe, Matthew Hale, Liang Zhao, and Pawel Ladosz. 2026. "Dynamic-LIVO: A Dynamic-Aware LiDAR-Inertial-Visual Odometry System Using Spatio-Temporal Normals." https://omanscience.com/en/articles/dynamic-livo-a-dynamic-aware-lidar-inertial-visual-odometry-system-using-spatio-temporal-normals.
Harvard
Zhang, Z., Ahiwe, S., Hale, M., Zhao, L. and Ladosz, P. (2026) 'Dynamic-LIVO: A Dynamic-Aware LiDAR-Inertial-Visual Odometry System Using Spatio-Temporal Normals', Available at: https://omanscience.com/en/articles/dynamic-livo-a-dynamic-aware-lidar-inertial-visual-odometry-system-using-spatio-temporal-normals.
Vancouver
Zhang Z, Ahiwe S, Hale M, Zhao L, Ladosz P. Dynamic-LIVO: A Dynamic-Aware LiDAR-Inertial-Visual Odometry System Using Spatio-Temporal Normals. https://omanscience.com/en/articles/dynamic-livo-a-dynamic-aware-lidar-inertial-visual-odometry-system-using-spatio-temporal-normals
IEEE
Z. Zhang, S. Ahiwe, M. Hale, L. Zhao, and P. Ladosz, "Dynamic-LIVO: A Dynamic-Aware LiDAR-Inertial-Visual Odometry System Using Spatio-Temporal Normals," https://omanscience.com/en/articles/dynamic-livo-a-dynamic-aware-lidar-inertial-visual-odometry-system-using-spatio-temporal-normals.