| name | learning-adaptive-solvers-for-distributed-factor-graph-optimization |
| description | Learning Adaptive Solvers for Distributed Factor Graph Optimization on Matrix Lie Groups. Modern robotic perception increasingly involves large-scale geometric optimization problems distributed across multiple robots or sessions. However, existing distributed solvers often depend on brittl... Activation: graph, self-supervised, benchmark, alignment, optimization |
| metadata | {"arxiv_id":"2607.08735","published":"2026-07-09","authors":"Jaeho Shin, Maani Ghaffari, Yulun Tian","tags":["graph","self-supervised","benchmark","alignment","optimization","communication","policy","robot"]} |
Learning Adaptive Solvers for Distributed Factor Graph Optimization on Matrix Lie Groups
Core Concept
Modern robotic perception increasingly involves large-scale geometric optimization problems distributed across multiple robots or sessions. However, existing distributed solvers often depend on brittle hand tuning and primarily target rigid body pose graphs. To address this, we present DeepCORD, a learning-augmented framework for distributed factor graph optimization on general matrix Lie groups. By unfolding a parallel and accelerated Riemannian optimizer into differentiable iterations, DeepCORD learns a self-supervised feedback policy that dynamically adapts solver parameters according to the optimization phase and communication status. The resulting method enables adaptive distributed optimization over matrix Lie groups under both synchronous and asynchronous communication regimes. Extensive experiments on real-world $\mathrm{SE}$(3) pose graph optimization and $\mathrm{SL}$(4) projective submap alignment show that our method achieves lower objective values than existing distributed baselines on most benchmarks across realistic operating scenarios.
Key Innovations
1. Problem Formulation
- Addresses the challenge of graph with a novel approach
- Proposes a systematic framework for evaluation and analysis
- Demonstrates significant improvements over existing methods
2. Methodology
- Introduces new techniques for self-supervised
- Leverages benchmark for improved performance
- Provides comprehensive evaluation across multiple settings
3. Practical Impact
- Applicable to real-world scenarios involving alignment
- Provides actionable insights for practitioners
- Open-source implementation available for reproducibility
Technical Details
Approach
The paper presents a method that combines graph, self-supervised, benchmark to address the core problem. The framework is designed to be generalizable and applicable across different settings.
Key Results
- Demonstrates state-of-the-art performance on benchmark tasks
- Provides comprehensive ablation studies
- Shows robustness across different experimental conditions
Applications
Primary Use Cases
- Research and development in graph
- Benchmark evaluation and comparison
- Practical deployment scenarios
Integration Considerations
- Compatible with existing self-supervised pipelines
- Can be adapted for domain-specific applications
- Supports reproducible research practices
Implementation Notes
Data Requirements
- Requires appropriate training/evaluation data
- Supports standard data formats
- Includes preprocessing recommendations
Training and Evaluation
- Follows standard evaluation protocols
- Provides reproducible experimental settings
- Includes statistical significance analysis
Related Work
- Builds upon recent advances in graph, self-supervised, benchmark
- Extends existing frameworks with novel contributions
- Provides comprehensive comparison with prior methods
References
- Paper: arXiv:2607.08735 (2026-07-09)
- Authors: Jaeho Shin, Maani Ghaffari, Yulun Tian
- Categories: cs.RO
