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.

翻译：环境中未知刚体的跟踪与建模在自主无人系统和虚实交互应用中起着关键作用。然而，现有多种同步定位、建图与动目标跟踪（SLAMMOT）方法仅专注于估计特定物体位姿，缺乏对物体尺度的估计，且无法有效跟踪未知物体。本文提出了一种新型SLAM后端，将自运动跟踪、刚体运动跟踪与建模整合于一个联合优化框架中。在感知部分，我们基于Segment Anything Model（SAM）和DeAOT设计了一种像素级异步物体跟踪器（AOT），使其能够在多种预定义任务和提示引导下有效跟踪目标未知物体。在建模部分，我们提出了一种新颖的以物体为中心的四维二次曲面参数化方法，统一了静态与动态物体的初始化及优化。随后，在物体状态估计部分，我们构建了一个物体位姿与尺度估计的紧耦合优化模型，将混合约束引入一种新颖的双滑窗优化框架实现联合估计。据我们所知，这是首次利用四维二次曲面将物体位姿跟踪与动静态物体轻量化建模进行紧耦合。我们在仿真数据集和真实数据集上开展了定性与定量实验，证明了该方法在运动估计与建模方面具备最先进的鲁棒性和精度。所提出的系统展示了物体感知在复杂动态场景中的潜在应用价值。