The pose-only (PO) visual representation has been proven to be equivalent to the classical multiple-view geometry, while significantly improving computational efficiency. However, its applicability for real-world navigation in large-scale complex environments has not yet been demonstrated. In this study, we present an efficient pose-only LiDAR-enhanced visual-inertial navigation system (PO-VINS) to enhance the real-time performance of the state estimator. In the visual-inertial state estimator (VISE), we propose a pose-only visual-reprojection measurement model that only contains the inertial measurement unit (IMU) pose and extrinsic-parameter states. We further integrated the LiDAR-enhanced method to construct a pose-only LiDAR-depth measurement model. Real-world experiments were conducted in large-scale complex environments, demonstrating that the proposed PO-VISE and LiDAR-enhanced PO-VISE reduce computational complexity by more than 50% and over 20%, respectively. Additionally, the PO-VINS yields the same accuracy as conventional methods. These results indicate that the pose-only solution is efficient and applicable for real-time visual-inertial state estimation.

翻译：仅位姿（PO）视觉表示已被证明与经典多视图几何等价，同时能显著提升计算效率。然而，其在复杂大尺度环境中的实际导航适用性尚未得到证实。本文提出一种高效的仅位姿激光雷达增强视觉惯性导航系统（PO-VINS），以提升状态估计器的实时性能。在视觉惯性状态估计器（VISE）中，我们提出一种仅包含惯性测量单元（IMU）位姿及外参状态的仅位姿视觉重投影测量模型。进一步融合激光雷达增强方法，构建了仅位姿激光雷达深度测量模型。在大尺度复杂环境中进行的实际实验表明，所提出的PO-VISE和激光雷达增强PO-VISE分别将计算复杂度降低50%以上和20%以上。此外，PO-VINS在精度上与常规方法相当。这些结果表明，仅位姿方案对于实时视觉惯性状态估计既高效又适用。