Four Simple Proprioceptive Estimators for Legged Robots
For legged robotics researchers, this provides a sequence of open-source estimators that trade off complexity and accuracy, but the improvements over existing methods are incremental.
This paper develops four proprioceptive state estimators for legged robots that use foot contact information to mitigate IMU drift, ranging from a contact-aided invariant EKF to a fixed-lag smoother with contact-episode footholds. All variants are implemented in GTSAM and ROS2 for reproducibility.
Legged robots carry an IMU, but the inertial solution drifts because consumer-grade IMUs are noisy. However, the feet create intermittent contacts with the environment that can be used to mitigate that drift. This report develops a sequence of increasingly expressive legged robot state estimators that leverage this. In all cases, the floating-base state comprises attitude, position, velocity, and IMU biases. To model foot contacts, we start from the contact-aided invariant EKF of Hartley et al., albeit at a reduced contact update rate. This is then augmented by replacing the measurement update by a small factor graph. Finally, we turn the same factors into a fixed-lag smoother with contact-episode footholds, with and without an evolving IMU bias. To facilitate reproducibility and further research in proprioceptive legged odometry, all four variants are available in GTSAM (Dellaert et. al), and we additionally provide a ROS2-compatible implementation.