Optimization-based visual-inertial simultaneous localization and mapping system (VI-SLAM) focuses on the establishment of the loss function using both inertial and visual constraints. Preintegration theory is commonly used to express inertial constraints, but it lacks the merging equation between keyframes, challenging VI-SLAM from culling and merging redundant keyframes. To address this, we establish an on-manifold preintegration merging theory, including the merging of preintegrated terms, noise covariance, and Jacobians for bias updating, which significantly improves the preintegration theory and provides theoretical support for the keyframe management function of VI-SLAM. Visual constraints are typically expressed using multiple view geometry with 3-D points optimized as scene structure parameters. However, the excessive dimensionality of the optimization parameters generated by 3-D points can lead to computational bottlenecks. Through the recent pose-only imaging geometry representation, we construct a lightweight optimization algorithm for SLAM that avoids the dimensional explosion in bundle adjustment. Based on the above, we propose a 3-D points-free SLAM optimizer. The proposed algorithms are validated on simulation, public datasets, and real-world experiments, and compared against advanced open-source systems, such as ORB-SLAM3 and VINS.

中文翻译：

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PIPO-SLAM：具有预集成合并理论和多视图几何的仅姿势描述的轻量级视觉惯性 SLAM

基于优化的视觉惯性同步定位与建图系统（VI-SLAM）侧重于利用惯性和视觉约束建立损失函数。预积分理论通常用于表达惯性约束，但它缺乏关键帧之间的合并方程，这给VI-SLAM带来了剔除和合并冗余关键帧的挑战。针对这一问题，我们建立了流形预积分合并理论，包括预积分项、噪声协方差和雅克比行列式的合并进行偏差更新，显着改进了预积分理论，为VI-SLAM的关键帧管理功能提供了理论支持。视觉约束通常使用多视图几何体来表达，其中 3D 点被优化为场景结构参数。然而，3D 点生成的优化参数维度过高可能会导致计算瓶颈。通过最近的仅位姿成像几何表示，我们构建了一种轻量级的 SLAM 优化算法，避免了捆绑调整中的维度爆炸。基于上述，我们提出了一种3D无点SLAM优化器。所提出的算法在模拟、公共数据集和现实实验中得到验证，并与先进的开源系统（例如 ORB-SLAM3 和 VINS）进行比较。