  Tracking and modeling unknown rigid objects in the environment play a crucial
role in autonomous unmanned systems and virtual-real interactive applications.
However, many existing Simultaneous Localization, Mapping and Moving Object
Tracking (SLAMMOT) methods focus solely on estimating specific object poses and
lack estimation of object scales and are unable to effectively track unknown
objects. In this paper, we propose a novel SLAM backend that unifies ego-motion
tracking, rigid object motion tracking, and modeling within a joint
optimization framework. In the perception part, we designed a pixel-level
asynchronous object tracker (AOT) based on the Segment Anything Model (SAM) and
DeAOT, enabling the tracker to effectively track target unknown objects guided
by various predefined tasks and prompts. In the modeling part, we present a
novel object-centric quadric parameterization to unify both static and dynamic
object initialization and optimization. Subsequently, in the part of object
state estimation, we propose a tightly coupled optimization model for object
pose and scale estimation, incorporating hybrids constraints into a novel dual
sliding window optimization framework for joint estimation. To our knowledge,
we are the first to tightly couple object pose tracking with light-weight
modeling of dynamic and static objects using quadric. We conduct qualitative
and quantitative experiments on simulation datasets and real-world datasets,
demonstrating the state-of-the-art robustness and accuracy in motion estimation
and modeling. Our system showcases the potential application of object
perception in complex dynamic scenes.
