Reproduce “Direct Visual Servoing Based on Discrete Orthogonal Moments” on OpenResearch
Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.
Universal app for Apple silicon and Intel. Requires macOS 11 or later.
About this paper
Yuhan Chen, Max Q.-H. Meng, Li Liu
A Direct Visual Servoing (DVS) framework leverages Discrete Orthogonal Moments (DOMs) as visual features, incorporating adaptive moment order and parameter selection. This approach significantly enhances DVS robustness and convergence, consistently succeeding in complex and noisy environments where other DVS methods struggle.
Key insights
- Discrete Orthogonal Moments effectively filter redundant image data, inherently improving DVS robustness against noise and occlusions and expanding the convergence domain.
- The adaptive parameter tuning of Hahn moments provides unparalleled flexibility, allowing the system to dynamically focus on global or local image information, leading to superior adaptability in diverse scenarios.
- Dynamically adjusting the DOM order `l` through a sigmoid function optimally balances fast initial convergence and high final positioning accuracy, adapting to the robot's proximity to the target pose.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.