Four photodiodes with optimized Gabor masks and an IMU, decoded by a simulator-trained TCN, deliver accurate planar odometry on differential-drive robots across indoor and outdoor terrains without real-world fine-tuning.
Tlio: Tight learned inertial odometry
3 Pith papers cite this work. Polarity classification is still indexing.
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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.
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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Minimalist Visual Inertial Odometry
Four photodiodes with optimized Gabor masks and an IMU, decoded by a simulator-trained TCN, deliver accurate planar odometry on differential-drive robots across indoor and outdoor terrains without real-world fine-tuning.
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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.