ARTI-VIO: Asynchronous Multi-Camera Visual-Inertial Odometry With Feature Recall and Time-Interpolated Optimization

Abstract

Multi-camera visual-inertial odometry (VIO) provides robust state estimation across wide fields of view, but asynchronous sensor streams without hardware synchronization introduce algorithmic and computational challenges. We present ARTI-VIO, a flexible framework for asynchronous multi-camera VIO with self-regulating stream rate control. In the front-end, we integrate a feature recall mechanism into the standard pipeline. Instead of discarding lost features, we temporarily retain them and re-associates them with newly extracted points via descriptor matching, thereby improving inter-frame feature consistency. In the back-end, we adopt a unified time-interpolated optimization framework that models asynchronous poses as interpolated functions over a sparse set of anchor poses, enabling the direct incorporation of misaligned observations as geometric constraints in the optimization. Experiments on a custom challenging dataset demonstrate that ARTIVIO maintains real-time performance across various camera configurations and achieves competitive accuracy and robustness compared with state-of-the-art baselines.