3 research outputs found
Contact-Aided Invariant Extended Kalman Filtering for Legged Robot State Estimation
This paper derives a contact-aided inertial navigation observer for a 3D
bipedal robot using the theory of invariant observer design. Aided inertial
navigation is fundamentally a nonlinear observer design problem; thus, current
solutions are based on approximations of the system dynamics, such as an
Extended Kalman Filter (EKF), which uses a system's Jacobian linearization
along the current best estimate of its trajectory. On the basis of the theory
of invariant observer design by Barrau and Bonnabel, and in particular, the
Invariant EKF (InEKF), we show that the error dynamics of the point
contact-inertial system follows a log-linear autonomous differential equation;
hence, the observable state variables can be rendered convergent with a domain
of attraction that is independent of the system's trajectory. Due to the
log-linear form of the error dynamics, it is not necessary to perform a
nonlinear observability analysis to show that when using an Inertial
Measurement Unit (IMU) and contact sensors, the absolute position of the robot
and a rotation about the gravity vector (yaw) are unobservable. We further
augment the state of the developed InEKF with IMU biases, as the online
estimation of these parameters has a crucial impact on system performance. We
evaluate the convergence of the proposed system with the commonly used
quaternion-based EKF observer using a Monte-Carlo simulation. In addition, our
experimental evaluation using a Cassie-series bipedal robot shows that the
contact-aided InEKF provides better performance in comparison with the
quaternion-based EKF as a result of exploiting symmetries present in the system
dynamics.Comment: Published in the proceedings of Robotics: Science and Systems 201
Hybrid Contact Preintegration for Visual-Inertial-Contact State Estimation Using Factor Graphs
The factor graph framework is a convenient modeling technique for robotic
state estimation where states are represented as nodes, and measurements are
modeled as factors. When designing a sensor fusion framework for legged robots,
one often has access to visual, inertial, joint encoder, and contact sensors.
While visual-inertial odometry has been studied extensively in this framework,
the addition of a preintegrated contact factor for legged robots has been only
recently proposed. This allowed for integration of encoder and contact
measurements into existing factor graphs, however, new nodes had to be added to
the graph every time contact was made or broken. In this work, to cope with the
problem of switching contact frames, we propose a hybrid contact preintegration
theory that allows contact information to be integrated through an arbitrary
number of contact switches. The proposed hybrid modeling approach reduces the
number of required variables in the nonlinear optimization problem by only
requiring new states to be added alongside camera or selected keyframes. This
method is evaluated using real experimental data collected from a Cassie-series
robot where the trajectory of the robot produced by a motion capture system is
used as a proxy for ground truth. The evaluation shows that inclusion of the
proposed preintegrated hybrid contact factor alongside visual-inertial
navigation systems improves estimation accuracy as well as robustness to vision
failure, while its generalization makes it more accessible for legged
platforms.Comment: Detailed derivations are provided in the supplementary material
document listed under "Ancillary files