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Legged Robot State Estimation using Invariant Kalman Filtering and Learned Contact Events
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This work develops a learning-based contact estimator for legged robots that bypasses the need for physical sensors and takes multi-modal proprioceptive sensory data as input. Unlike vision-based state estimators, proprioceptive state estimators are agnostic to perceptually degraded situations such as dark or foggy scenes. While some robots are equipped with dedicated physical sensors to detect necessary contact data for state estimation, some robots do not have dedicated contact sensors, and the addition of such sensors is non-trivial without redesigning the hardware. The trained network can estimate contact events on different terrains. The experiments show that a contact-aided invariant extended Kalman filter can generate accurate odometry trajectories compared to a state-of-the-art visual SLAM system, enabling robust proprioceptive odometry.
Forward citations
Cited by 3 Pith papers
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Chalito: An Extensible Library for Filtering-Based State Estimation in Quadruped Robots
Chalito is the first open-source library dedicated to benchmarking filter-based state estimators for quadruped robots across robots and datasets via URDF-driven, filter-agnostic interfaces.
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Interacting Multiple Model Proprioceptive Odometry for Legged Robots
An IMM filter whose modes differ only in foot-velocity noise, combined with a rolling-contact measurement model, cuts drift in IMU/encoder-only quadruped odometry by 30-70% in simulation and ~55% on a real robot.
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Noise Analysis and Hierarchical Adaptive Body State Estimator For Biped Robot Walking With ESVC Foot
A two-stage adaptive state estimator with a noise regression model built from ESVC-foot biped experiments estimates CoM state with faster convergence than EKF and an adaptive EKF during marking time and variable-speed...
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