Traditional Visual Simultaneous Localization and Mapping 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 developedDynoSAM, an open-source framework for Dynamic Objects SLAM that enables the efficient implementation, testing, and comparison of various Dynamic SLAM optimization formulations. We further propose a novel formulation that encodes rigid-body motion model in object pose estimation as well as an error metric agnostic to object frame definition.DynoSAMintegrates 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 evaluateDynoSAMacross 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 demonstrateDynoSAM's contributions to downstream applications, including 3D reconstruction of dynamic scenes and trajectory prediction, thereby showcasing potential for advancing dynamic object-aware SLAM systems. Code is open-sourced athttps://github.com/ACFR-RPG/DynOSAM