DOGL-SLAM: Dynamic Object-Level SLAM via Joint Gaussian-Landmark Tracking
Songyang Wu, Xuebo Zhang, Shiyong Zhang, Runzhao Yao, Zhixing Song, Yunze Tong, Jing Yuan
Abstract
Recent advancements in 3D Gaussian Splatting (3DGS) have significantly improved the mapping quality and computational efficiency of visual Simultaneous Localization and Mapping (SLAM). We propose DOGL-SLAM, a novel framework that integrates 3DGS into its core pipeline, enabling accurate camera pose tracking, object-level interaction, and high-fidelity scene reconstruction in dynamic environments. Firstly, a joint graph optimization module incorporates both dense Gaussian and sparse landmark constraints, enabling precise alignment between camera tracking and mapping. Secondly, a consistent object-level semantic fusion module embeds category labels into 3D Gaussians, grouping them via semantic consistency and optimizing distributions through cross-view losses to support scene manipulation. Finally, a hierarchical dynamic filtering pipeline is introduced, consisting of segmentation, dynamic feature exclusion, gradient masking, and visibility-aware Gaussian pruning across three parallel threads. Our system is evaluated across multiple datasets, showing a significant improvement in high-quality view synthesis for dynamic scenes. Additionally, the generated object-level semantic maps facilitate advanced downstream tasks, highlighting the potential of a robust SLAM framework. The source code and supplementary video will be released.
BibTeX
@inproceedings{ral2026_doglslamdynamico,
title = {DOGL-SLAM: Dynamic Object-Level SLAM via Joint Gaussian-Landmark Tracking},
author = {Songyang Wu and Xuebo Zhang and Shiyong Zhang and Runzhao Yao and Zhixing Song and Yunze Tong and Jing Yuan},
booktitle = {RA-L 2026},
year = {2026}
}