Overlapping Domain Decomposition for Distributed Pose Graph Optimization
Aneesa Sonawalla, Yulun Tian, Jonathan How
AI summary
Problem
Existing distributed pose graph optimization methods minimize inter-robot communication but converge slowly, failing to leverage the high-bandwidth networks available on modern robots.
Approach
ROBO enables robots to share overlapping subsets of poses from each other's domains during optimization, creating inflated local blocks that accelerate convergence while allowing a tunable trade-off between communication bandwidth and speed.
Key results
- Converges 3.1× faster in iterations than state-of-the-art distributed PGO methods
- Achieves accelerated convergence with an average inter-robot data cost of only 36 Kb per iteration
- Introduces an asynchronous variant robust to real-world network delays
- Demonstrates adaptability across diverse initialization schemes, cost functions, and communication regimes on benchmark datasets
Why it matters
It enables scalable, high-precision multi-robot localization in GPS-denied environments by efficiently leveraging modern high-bandwidth networks without sacrificing distributed privacy or robustness.
Abstract
We present ROBO (Riemannian Overlapping Block Optimization), a distributed and parallel approach to multi-robot pose graph optimization (PGO) based on the idea of overlapping domain decomposition. ROBO offers a middle ground between centralized and fully distributed solvers, where the amount of pose information shared between robots at each optimization iteration can be set according to the available communication resources. Sharing additional pose information between neighboring robots effectively creates overlapping op- timization blocks in the underlying pose graph, which substan- tially reduces the number of iterations required to converge. Through extensive experiments on benchmark PGO datasets, we demonstrate the applicability and feasibility of ROBO in different initialization scenarios, using various cost functions, and under different communication regimes. We also analyze the tradeoff between the increased communication and local computation required by ROBO’s overlapping blocks and the resulting faster convergence. We show that overlaps with an average inter-robot data cost of only 36 Kb per iteration can converge 3.1× faster in terms of iterations than state-of-the- art distributed PGO approaches. Furthermore, we develop an asynchronous variant of ROBO that is robust to network delays and suitable for real-world robotic applications.