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Found 3 papers, 1 papers with code
IDOL: Inertial Deep Orientation-Estimation and Localization
Many smartphone applications use inertial measurement units (IMUs) to sense movement, but the use of these sensors for pedestrian localization can be challenging due to their noise characteristics.
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.
xFBD: Focused Building Damage Dataset and Analysis
This new challenge involves a new dataset and metrics indicating solution performance when damage is more local and limited than in xBD.
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