  In this paper, we propose a method for online estimation of the robot's
posture. Our method uses von Mises and Bingham distributions as probability
distributions of joint angles and 3D orientation, which are used in directional
statistics. We constructed a particle filter using these distributions and
configured a system to estimate the robot's posture from various sensor
information (e.g., joint encoders, IMU sensors, and cameras). Furthermore,
unlike tangent space approximations, these distributions can handle global
features and represent sensor characteristics as observation noises. As an
application, we show that the yaw drift of a 6-axis IMU sensor can be
represented probabilistically to prevent adverse effects on attitude
estimation. For the estimation, we used an approximate model that assumes the
actual robot posture can be reproduced by correcting the joint angles of a
rigid body model. In the experiment part, we tested the estimator's
effectiveness by examining that the joint angles generated with the approximate
model can be estimated using the link pose of the same model. We then applied
the estimator to the actual robot and confirmed that the gripper position could
be estimated, thereby verifying the validity of the approximate model in our
situation.
