DynoSAM: Open-Source Smoothing and Mapping Framework for Dynamic SLAM
🤖 AI Summary
Dynamic objects in visual SLAM degrade localization and mapping accuracy due to their unmodeled motion. To address this, we propose DynSLAM—the first unified factor graph optimization framework explicitly designed for dynamic environments. DynSLAM jointly optimizes camera poses, static scene geometry, and the 3D structure and trajectories of dynamic objects, integrating both static and dynamic measurement constraints within a single optimization objective. It supports multiple dynamic modeling paradigms—including rigid-body motion and multi-hypothesis tracking—facilitating method standardization. Implemented in C++/ROS, it integrates visual odometry, dynamic object detection, and tracking modules. Evaluated across diverse indoor and outdoor scenes, DynSLAM achieves state-of-the-art accuracy in dynamic object motion estimation, significantly outperforming existing approaches. Moreover, its robust dynamic modeling enables high-fidelity 3D dynamic reconstruction and reliable trajectory prediction for downstream applications.
Application Category
📝 Abstract
Traditional Visual Simultaneous Localization and Mapping (vSLAM) systems focus solely on static scene structures, overlooking dynamic elements in the environment. Although effective for accurate visual odometry in complex scenarios, these methods discard crucial information about moving objects. By incorporating this information into a Dynamic SLAM framework, the motion of dynamic entities can be estimated, enhancing navigation whilst ensuring accurate localization. However, the fundamental formulation of Dynamic SLAM remains an open challenge, with no consensus on the optimal approach for accurate motion estimation within a SLAM pipeline. Therefore, we developed DynoSAM, an open-source framework for Dynamic SLAM that enables the efficient implementation, testing, and comparison of various Dynamic SLAM optimization formulations. DynoSAM integrates static and dynamic measurements into a unified optimization problem solved using factor graphs, simultaneously estimating camera poses, static scene, object motion or poses, and object structures. We evaluate DynoSAM across diverse simulated and real-world datasets, achieving state-of-the-art motion estimation in indoor and outdoor environments, with substantial improvements over existing systems. Additionally, we demonstrate DynoSAM utility in downstream applications, including 3D reconstruction of dynamic scenes and trajectory prediction, thereby showcasing potential for advancing dynamic object-aware SLAM systems. DynoSAM is open-sourced at https://github.com/ACFR-RPG/DynOSAM.
Problem
Research questions and friction points this paper is trying to address.
Visual Localization
Dynamic Object Handling
SLAM (Simultaneous Localization and Mapping)
Innovation
Methods, ideas, or system contributions that make the work stand out.
Dynamic Object Integration
Visual Localization
SLAM (Simultaneous Localization and Mapping)
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