RMSC-VIO: Robust Multi-Stereoscopic Visual-Inertial Odometry for Local Visually Challenging Scenarios

We present a Multi-Stereoscopic Visual-Inertial Odometry (VIO) system capable of integrating an arbitrary num- ber 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 op- timization 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 experi- ments. Our approach achieves up to a 90% reduction in Absolute Trajectory Error (ATE) compared to state-of-the-art multi-camera VIO methods.