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.
SlideSLAM : Sparse , Lightweight , Decentralized Metric - Semantic SLAM for Multi - Robot Navigation , July 2024
2 Pith papers cite this work, alongside 1 external citations. Polarity classification is still indexing.
2
Pith papers citing it
1
external citations · external index
citation-role summary
background 1
citation-polarity summary
fields
cs.RO 2years
2026 2roles
background 1polarities
background 1representative citing papers
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.
citing papers explorer
-
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.
-
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.