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.
Learned inertial odometry for autonomous drone racing
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An inertial navigation system for bikes fuses mixture-of-experts learning with pedal-to-wheel mechanical constraints to reduce drift, reporting at least 12% accuracy gain and sub-0.5 m/s wheel-speed error on real DiDi ride data.
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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Tracking Large-scale Shared Bikes with Inertial Motion Learning in GNSS Blocked Environments
An inertial navigation system for bikes fuses mixture-of-experts learning with pedal-to-wheel mechanical constraints to reduce drift, reporting at least 12% accuracy gain and sub-0.5 m/s wheel-speed error on real DiDi ride data.