Self-supervised contact detection for legged robot odometry using joint encoders outperforms supervised methods that require force sensors.
Legged robot state estimation using invariant kalman filtering and learned contact events
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
fields
cs.RO 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
OCELOT fuses a debounced force GMM-FSM and kinematic GLRT into an ESEKF to produce accurate leg odometry from IMU, encoders and force sensors while explicitly detecting and rejecting slippage on diverse terrains.
An IterIEKF algorithm for quadruped odometry, relying on proprioceptive kinematic constraints, outperforms vanilla IEKF and SO(3) Kalman filters in accuracy and consistency on simulations and real datasets.
citing papers explorer
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Learning Contact Representation for Leg Odometry
Self-supervised contact detection for legged robot odometry using joint encoders outperforms supervised methods that require force sensors.
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OCELOT: Odometry and Contact Estimation for Legged Robots
OCELOT fuses a debounced force GMM-FSM and kinematic GLRT into an ESEKF to produce accurate leg odometry from IMU, encoders and force sensors while explicitly detecting and rejecting slippage on diverse terrains.
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Iterated Invariant EKF for Quadruped Robot Odometry
An IterIEKF algorithm for quadruped odometry, relying on proprioceptive kinematic constraints, outperforms vanilla IEKF and SO(3) Kalman filters in accuracy and consistency on simulations and real datasets.