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Accurate and robust localization is a fundamental need for mobile agents.Visual-inertial odometry (VIO) algorithms exploit the information from cameraand inertial sensors to estimate position and translation. Recent deep learningbased VIO models attract attentions as they provide pose information in adata-driven way, without the need of designing hand-crafted algorithms.Existing learning based VIO models rely on recurrent models to fuse multimodaldata and process sensor signal, which are hard to train and not efficientenough. We propose a novel learning based VIO framework with external memoryattention that effectively and efficiently combines visual and inertialfeatures for states estimation. Our proposed model is able to estimate poseaccurately and robustly, even in challenging scenarios, e.g., on overcast daysand water-filled ground , which are difficult for traditional VIO algorithms toextract visual features. Experiments validate that it outperforms bothtraditional and learning based VIO baselines in different scenes.

EMA-VIO: Deep Visual-Inertial Odometry with External Memory Attention