  Six axis force-torque sensors are commonly attached to the wrist of serial
robots to measure the external forces and torques acting on the robot's
end-effector. These measurements are used for load identification, contact
detection, and human-robot interaction amongst other applications. Typically,
the measurements obtained from the force-torque sensor are more accurate than
estimates computed from joint torque readings, as the former is independent of
the robot's dynamic and kinematic models. However, the force-torque sensor
measurements are affected by a bias that drifts over time, caused by the
compounding effects of temperature changes, mechanical stresses, and other
factors. In this work, we present a pipeline that continuously estimates the
bias and the drift of the bias of a force-torque sensor attached to the wrist
of a robot. The first component of the pipeline is a Kalman filter that
estimates the kinematic state (position, velocity, and acceleration) of the
robot's joints. The second component is a kinematic model that maps the
joint-space kinematics to the task-space kinematics of the force-torque sensor.
Finally, the third component is a Kalman filter that estimates the bias and the
drift of the bias of the force-torque sensor assuming that the inertial
parameters of the gripper attached to the distal end of the force-torque sensor
are known with certainty.
