Pose graph optimization is recast as damped Riemannian dynamics on Lie groups, enabling a fully distributed algorithm with a semi-implicit integrator that converges under both synchronous and asynchronous communication.
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2 Pith papers cite this work, alongside 3 external citations. Polarity classification is still indexing.
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A learned feedback policy replaces manual parameter tuning in distributed Riemannian optimization over matrix Lie groups, achieving lower objective values on multi-robot mapping benchmarks.
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Distributed Pose Graph Optimization via Continuous Riemannian Dynamics
Pose graph optimization is recast as damped Riemannian dynamics on Lie groups, enabling a fully distributed algorithm with a semi-implicit integrator that converges under both synchronous and asynchronous communication.
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Learning Adaptive Solvers for Distributed Factor Graph Optimization on Matrix Lie Groups
A learned feedback policy replaces manual parameter tuning in distributed Riemannian optimization over matrix Lie groups, achieving lower objective values on multi-robot mapping benchmarks.