Conceitos Básicos
Developing a fully distributed, asynchronous, and general purpose optimization algorithm for CSLAM using Consensus ADMM called MESA.
Resumo
This paper introduces MESA, a distributed algorithm for solving CSLAM problems. It focuses on the back-end optimization of noisy measurements for multi-robot teams. The algorithm is designed to be fully distributed, asynchronous, and general purpose to handle various CSLAM problem formulations. By utilizing Consensus ADMM, MESA demonstrates superior convergence rates and accuracy compared to existing optimizers.
I. INTRODUCTION
- Collaborative teams of autonomous robots are essential in various applications.
- CSLAM is crucial for multi-robot teams to have accurate state estimates.
- The CSLAM back-end is responsible for composing noisy measurements into a state estimate.
II. RELATED WORK
- Previous work has explored distributed optimization algorithms like C-ADMM.
- Various methods have been proposed for solving generic CSLAM problems.
- Different approaches such as Loopy Belief Propagation and Pose Graph Optimization have been investigated.
III. CONSENSUS ADMM
Key concept: Consensus ADMM is a fully distributed optimization method that enforces agreement among neighboring agents in a network.
IV. METHODOLOGY
- Define the CSLAM problem as Maximum-A-Posteriori inference.
- Introduce Manifold, Edge-based, Separable ADMM (MESA) as an efficient algorithm for solving CSLAM problems with asynchronous communication.
V. EXPERIMENTS
- MESA Variant Exploration:
- Geodesic and Split variants of MESA outperform other constraint functions.
- MESA Generalization:
- Both Geodesic and Split MESA variants demonstrate accuracy and convergence across different CSLAM scenarios.
- Prior Work Comparison:
- MESA achieves superior accuracy and faster convergence compared to existing methods like DGS, ASAPP, MB-ADMM, and DDF-SAM2.
Estatísticas
Recent work has proven that non-convex C-ADMM will converge under certain assumptions.