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.
Distributed Pose - Graph Optimization With Multi - Level Partitioning for Multi - Robot SLAM
3 Pith papers cite this work, alongside 9 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.
Hybrid ME-DDP variants combine deterministic DDP with inverse-Hessian sampling to improve success rates over pure DDP and MPPI in robotic navigation under non-convex costs.
citing papers explorer
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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.
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Beyond Pure Sampling: Hybrid Optimization Mechanisms for Non-Convex Model Predictive Control
Hybrid ME-DDP variants combine deterministic DDP with inverse-Hessian sampling to improve success rates over pure DDP and MPPI in robotic navigation under non-convex costs.