PO-GVINS: 采用纯位姿表示的紧耦合GNSS-视觉-惯性融合系统
PO-GVINS: Tightly Coupled GNSS-Visual-Inertial Integration with Pose-Only Representation
- Hubei Luojia Laboratory（湖北珞珈实验室）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对现有VINS多视图成像存在线性化误差与维度爆炸的问题，提出采用纯位姿表示的紧耦合GNSS-视觉-惯性定位框架PO-GVINS，实验证明其性能优于MSCKF，可实现精准无漂移的定位。
AI中文摘要：
精准可靠的定位对自动驾驶、无人机及智能机器人中的感知、决策等高层应用至关重要。鉴于单一传感器存在固有局限，融合具有互补特性的异构传感器是实现该目标的最有效途径之一。本文提出一种基于滤波的紧耦合全球导航卫星系统（GNSS）-视觉-惯性定位框架，该框架在视觉惯性系统（VINS）中采用纯位姿公式，命名为PO-GVINS。具体而言，当前VINS中使用的多视图成像需要3D特征的先验信息，再联合估计相机位姿与3D特征位置，这不可避免地会引入特征的线性化误差，同时还面临维度爆炸问题。而纯位姿（PO）公式已被证明与多视图成像等价，且已应用于视觉重建领域，它利用两个相机位姿表示特征深度，从而将3D特征位置从状态向量中移除，避免了上述难题。受此启发，我们首先将PO公式应用于我们的VINS中，即PO-VINS。随后引入已解算整周模糊度的GNSS原始测量值，以实现精准且无漂移的估计。大量实验表明，所提出的PO-VINS显著优于多状态约束卡尔曼滤波（MSCKF）。通过融合GNSS测量值，PO-GVINS可实现精准、无漂移的状态估计，是挑战性环境下定位的可靠解决方案。
英文摘要：
Accurate and reliable positioning is crucial for perception, decision-making, and other high-level applications in autonomous driving, unmanned aerial vehicles, and intelligent robots. Given the inherent limitations of standalone sensors, integrating heterogeneous sensors with complementary capabilities is one of the most effective approaches to achieving this goal. In this paper, we propose a filtering-based, tightly coupled global navigation satellite system (GNSS)-visual-inertial positioning framework with a pose-only formulation applied to the visual-inertial system (VINS), termed PO-GVINS. Specifically, multiple-view imaging used in current VINS requires a priori of 3D feature, then jointly estimate camera poses and 3D feature position, which inevitably introduces linearization error of the feature as well as facing dimensional explosion. However, the pose-only (PO) formulation, which is demonstrated to be equivalent to the multiple-view imaging and has been applied in visual reconstruction, represent feature depth using two camera poses and thus 3D feature position is removed from state vector avoiding aforementioned difficulties. Inspired by this, we first apply PO formulation in our VINS, i.e., PO-VINS. GNSS raw measurements are then incorporated with integer ambiguity resolved to achieve accurate and drift-free estimation. Extensive experiments demonstrate that the proposed PO-VINS significantly outperforms the multi-state constrained Kalman filter (MSCKF). By incorporating GNSS measurements, PO-GVINS achieves accurate, drift-free state estimation, making it a robust solution for positioning in challenging environments.
