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：环境中未知刚性物体的跟踪与建模在自主无人系统及虚实交互应用中扮演关键角色。然而，现有同步定位、建图与移动物体跟踪（SLAMMOT）方法大多仅专注于估计特定物体位姿，缺乏对物体尺度的估计，且难以有效跟踪未知物体。本文提出一种新型SLAM后端，将自运动跟踪、刚性物体运动跟踪与建模统一于联合优化框架中。在感知模块，我们基于Segment Anything Model（SAM）和DeAOT设计了像素级异步物体跟踪器（AOT），使其能有效跟踪由多种预定义任务与提示引导的目标未知物体。在建模模块，我们提出新颖的以物体为中心的二次曲面参数化方法，统一了静态与动态物体的初始化和优化。随后，在物体状态估计模块中，我们提出一种紧耦合的物体位姿与尺度估计优化模型，将混合约束融入新型双滑动窗口优化框架实现联合估计。据我们所知，这是首个利用二次曲面实现物体位姿跟踪与动静态物体轻量化建模紧耦合的工作。我们在仿真数据集与真实数据集上开展了定性与定量实验，展示了运动估计与建模方面最先进的鲁棒性与精度。本系统展示了物体感知在复杂动态场景中的潜在应用价值。