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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.
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In this work, we present a different approach to determine the pose using angular measurements discontinuously obtained in time.
The proposed planner automatically determines the pose of an object that is stably placed near a user-assigned point on the environment surface.
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.
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.
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).
"In situ augmentation" describes a method for determining the camera pose and registering 3D models with real world objects.
After determining the camera pose, the next step is to find planar objects, to be augmented, in this work, by synthetic column models.
If more than one software/algorithm is used for obtaining docked complexes, then the complexity of determining the correct pose further increases as the best predicted models by each algorithm can be entirely different.
Whatever the pictures' purpose, and whoever determined the poses, the result is portraiture rather than pornography.
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