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Found 2 papers, 1 papers with code
RP-VIO: Robust Plane-based Visual-Inertial Odometry for Dynamic Environments
We evaluate our approach on this dataset, and three diverse sequences from standard datasets including two real-world dynamic sequences and show a significant improvement in robustness and accuracy over a state-of-the-art monocular visual-inertial odometry system.
Learnable Spatio-Temporal Map Embeddings for Deep Inertial Localization
To address the robustness problem in map utilization, we propose a data-driven prior on possible user locations in a map by combining learned spatial map embeddings and temporal odometry embeddings.
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