1,703 research outputs found
Recursive Estimation of Orientation Based on the Bingham Distribution
Directional estimation is a common problem in many tracking applications.
Traditional filters such as the Kalman filter perform poorly because they fail
to take the periodic nature of the problem into account. We present a recursive
filter for directional data based on the Bingham distribution in two
dimensions. The proposed filter can be applied to circular filtering problems
with 180 degree symmetry, i.e., rotations by 180 degrees cannot be
distinguished. It is easily implemented using standard numerical techniques and
suitable for real-time applications. The presented approach is extensible to
quaternions, which allow tracking arbitrary three-dimensional orientations. We
evaluate our filter in a challenging scenario and compare it to a traditional
Kalman filtering approach
Unscented Orientation Estimation Based on the Bingham Distribution
Orientation estimation for 3D objects is a common problem that is usually
tackled with traditional nonlinear filtering techniques such as the extended
Kalman filter (EKF) or the unscented Kalman filter (UKF). Most of these
techniques assume Gaussian distributions to account for system noise and
uncertain measurements. This distributional assumption does not consider the
periodic nature of pose and orientation uncertainty. We propose a filter that
considers the periodicity of the orientation estimation problem in its
distributional assumption. This is achieved by making use of the Bingham
distribution, which is defined on the hypersphere and thus inherently more
suitable to periodic problems. Furthermore, handling of non-trivial system
functions is done using deterministic sampling in an efficient way. A
deterministic sampling scheme reminiscent of the UKF is proposed for the
nonlinear manifold of orientations. It is the first deterministic sampling
scheme that truly reflects the nonlinear manifold of the orientation
A surgical system for automatic registration, stiffness mapping and dynamic image overlay
In this paper we develop a surgical system using the da Vinci research kit
(dVRK) that is capable of autonomously searching for tumors and dynamically
displaying the tumor location using augmented reality. Such a system has the
potential to quickly reveal the location and shape of tumors and visually
overlay that information to reduce the cognitive overload of the surgeon. We
believe that our approach is one of the first to incorporate state-of-the-art
methods in registration, force sensing and tumor localization into a unified
surgical system. First, the preoperative model is registered to the
intra-operative scene using a Bingham distribution-based filtering approach. An
active level set estimation is then used to find the location and the shape of
the tumors. We use a recently developed miniature force sensor to perform the
palpation. The estimated stiffness map is then dynamically overlaid onto the
registered preoperative model of the organ. We demonstrate the efficacy of our
system by performing experiments on phantom prostate models with embedded stiff
inclusions.Comment: International Symposium on Medical Robotics (ISMR 2018
Online Estimation of Self-Body Deflection With Various Sensor Data Based on Directional Statistics
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.Comment: This work has been submitted to the IEEE for possible publication.
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