591 research outputs found
Non-singular Cooperative Guiding Vector Field Under a Homotopy Equivalence Transformation
The present article advances the concept of a non-singular cooperative
guiding vector field under a homotopy equivalence transformation. Firstly, the
derivation of a guiding vector field, based on a non-singular vector field, is
elaborated to navigate a transformed path from another frame. The existence of
such vector fields is also deliberated herein. Subsequently, a coordination
vector field derived from the guiding vector field is presented, incorporating
an in-depth analysis concerning the impact of the vector field parameters.
Lastly, the practical implementation of this novel vector field is demonstrated
by its applications to 2-D and 3-D cooperative moving path following issues,
establishing its efficacy.Comment: 12 pages, 12 figures, submitting to TAC at presen
A Frequency-Domain Path-Following Method for Discrete Data-Based Paths
This paper presents a novel frequency-domain approach for path following
problem, specifically designed to handle paths described by discrete data. The
proposed algorithm utilizes the fast Fourier Transform (FFT) to process the
discrete path data, enabling the construction of a non-singular guiding vector
field. This vector field serves as a reference direction for the controlled
robot, offering the ability to adapt to different levels of precision.
Additionally, the frequency-domain nature of the vector field allows for the
reduction of computational complexity and effective noise suppression. The
efficacy of the proposed approach is demonstrated through a numerical
simulation, and theoretical analysis provides an upper bound for the ultimate
mean-square path-following error
Direct-PoseNet: Absolute Pose Regression with Photometric Consistency
We present a relocalization pipeline, which combines an absolute pose
regression (APR) network with a novel view synthesis based direct matching
module, offering superior accuracy while maintaining low inference time. Our
contribution is twofold: i) we design a direct matching module that supplies a
photometric supervision signal to refine the pose regression network via
differentiable rendering; ii) we modify the rotation representation from the
classical quaternion to SO(3) in pose regression, removing the need for
balancing rotation and translation loss terms. As a result, our network
Direct-PoseNet achieves state-of-the-art performance among all other
single-image APR methods on the 7-Scenes benchmark and the LLFF dataset
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