Exact(4)
In this work, we present a different approach to determine the pose using angular measurements discontinuously obtained in time.
In this paper, a method based on two collaborative cameras is proposed to determine the pose (position and orientation) of a large non-cooperative target.
A recurring problem with AAM-based synthesis and multiview methods is the need to reliably locate facial features to determine the pose angle for pose compensation this turns out to a be difficult task in its own right.
Moreover, as is shown in Figure 12, the alignment algorithm can also be used to determine the pose of an object in a cluttered scene (problem that arises in bin-picking tasks).
Similar(56)
This paper proposes an Adaptive Multi-space Transformation (AMT) method to assist the operator to improve the accuracy and reliability in determining the pose of the robot.
The proposed planner automatically determines the pose of an object that is stably placed near a user-assigned point on the environment surface.
By measuring the time difference between the returned signals from the target surface, which are generated by two transmitters with 1 ms difference, it classifies the type and determines the pose of the target surface.
To determine the head pose, face images with varying pose angles can be considered to be lying on a smooth low-dimensional manifold in high-dimensional image feature space.
To manipulate an object located by the ARToolKit, we determine the hand pose following these steps: 1. Estimate the position of each object in the coordinate system of the camera mounted on the robot, and 2.
This continuous angular estimation allows the consistent use of the triangulation methods to determine the robot pose at any time during its motion.
For this reason macromolecular docking remains the favourite method to determine the relative pose in between a receptor and a ligand.
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