X-IONet combines rule-based platform classification with a dual-stage attention network to predict displacement and uncertainty from IMU data, then fuses outputs via EKF, achieving reported error reductions on pedestrian and quadruped datasets.
Eqnio: Subequivariant neural inertial odometry
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MARIO achieves up to 42% reduction in positional drift for inertial odometry by using a learned IMU-inferred pose prior and fusing data from additional sensors on AR glasses.
citing papers explorer
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X-IONet: Cross-Platform Inertial Odometry Network for Pedestrian and Legged Robot
X-IONet combines rule-based platform classification with a dual-stage attention network to predict displacement and uncertainty from IMU data, then fuses outputs via EKF, achieving reported error reductions on pedestrian and quadruped datasets.
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MARIO: Motion-Augmented Real-Time Multi-Sensor Inertial Odometry
MARIO achieves up to 42% reduction in positional drift for inertial odometry by using a learned IMU-inferred pose prior and fusing data from additional sensors on AR glasses.