We present a Multi-Stereoscopic Visual-Inertial Odometry (VIO) system capable of integrating an arbitrary number of stereo cameras, exhibiting excellent robustness in the face of visually challenging scenarios. During system initialization, we introduce multi-view keyframes for simultaneous processing of multiple image inputs and propose an adaptive feature selection method to alleviate the computational burden of multi-camera systems. This method iteratively updates the state information of visual features, filtering out high-quality image feature points and effectively reducing unnecessary redundancy consumption. In the backend phase, we propose an adaptive tightly coupled optimization method, assigning corresponding optimization weights based on the quality of different image feature points, effectively enhancing localization precision. We validate the effectiveness and robustness of our system through a series of datasets, encompassing various visually challenging scenarios and practical flight experiments. Our approach achieves up to a 90% reduction in Absolute Trajectory Error (ATE) compared to state-of-the-art multi-camera VIO methods.

中文翻译：

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RMSC-VIO：适用于局部视觉挑战场景的稳健多立体视觉惯性里程计

我们提出了一种多立体视觉惯性里程计（VIO）系统，能够集成任意数量的立体摄像机，在面对视觉挑战的场景时表现出出色的鲁棒性。在系统初始化期间，我们引入了多视图关键帧来同时处理多个图像输入，并提出了一种自适应特征选择方法来减轻多相机系统的计算负担。该方法迭代更新视觉特征的状态信息，过滤掉高质量的图像特征点，有效减少不必要的冗余消耗。在后端阶段，我们提出了一种自适应紧耦合优化方法，根据不同图像特征点的质量分配相应的优化权重，有效提高定位精度。我们通过一系列数据集验证系统的有效性和鲁棒性，包括各种具有视觉挑战性的场景和实际飞行实验。与最先进的多摄像头 VIO 方法相比，我们的方法可将绝对轨迹误差 (ATE) 降低高达 90%。