Efficient Visual-Inertial Odometry (VIO) is crucial for payload-constrained robots. Though modern optimization-based algorithms have achieved superior accuracy, the MSCKF-based VIO algorithms are still widely demanded for their efficient and consistent performance. As MSCKF is built upon the conventional multi-view geometry, the measured residuals are not only related to the state errors but also related to the feature position errors. To apply EKF fusion, a projection process is required to remove the feature position error from the observation model, which can lead to model and accuracy degradation. To obtain an efficient visual-inertial fusion model, while also preserving the model consistency, we propose to reconstruct the MSCKF VIO with the novel Pose-Only (PO) multi-view geometry description. In the newly constructed filter, we have modeled PO reprojection residuals, which are solely related to the motion states and thus overcome the requirements of space projection. Moreover, the new filter does not require any feature position information, which removes the computational cost and linearization errors brought in by the 3D reconstruction procedure. We have conducted comprehensive experiments on multiple datasets, where the proposed method has shown accuracy improvements and consistent performance in challenging sequences.

翻译：高效视觉惯性里程计对于载荷受限的机器人至关重要。尽管现代基于优化的算法已实现卓越精度，但基于MSCKF的VIO算法因其高效且一致的性能仍被广泛需求。由于MSCKF建立在传统多视图几何基础上，其测量残差不仅与状态误差相关，还与特征点位置误差相关。为应用EKF融合，需通过投影过程从观测模型中消除特征位置误差，这可能导致模型与精度退化。为获得高效的视觉惯性融合模型并保持模型一致性，我们提出采用新颖的纯位姿多视图几何描述重构MSCKF VIO。在新构建的滤波器中，我们建立了纯位姿重投影残差模型，该残差仅与运动状态相关，从而克服了空间投影的需求。此外，新滤波器无需任何特征点位置信息，消除了三维重建过程带来的计算成本与线性化误差。我们在多个数据集上进行了全面实验，所提方法在挑战性序列中展现出精度提升与一致性能。