通过重叠域分解加速多机器人位姿图优化，通信少、收敛快。
Overlapping Domain Decomposition for Distributed Pose Graph Optimization
- 采用重叠域分解思想，动态调节机器人间共享信息量。
- 每轮通信仅需36KB数据，迭代次数减少3.1倍。
- 支持异步运行，适合实际机器人网络应用。
我们提出ROBO（黎曼重叠块优化），一种基于重叠域分解的分布式并行多机器人位姿图优化方法。ROBO在集中式与完全分布式解法之间取得平衡，可根据通信资源灵活设置每轮优化中机器人间共享的位姿信息量。通过在相邻机器人间共享额外信息，形成重叠的优化块，显著减少收敛所需迭代次数。在多个基准位姿图优化数据集上的大量实验表明，ROBO在不同初始化、多种代价函数和通信条件下均具可行性。我们分析了重叠块带来的通信与本地计算开销与加速收敛之间的权衡：平均每轮仅需36KB的跨机器人数据传输，即可实现相比最先进分布式方法3.1倍的迭代速度提升。此外，我们还开发了对网络延迟鲁棒的异步版本，适用于真实机器人应用场景。
原文摘要 · Abstract (English)
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 optimization blocks in the underlying pose graph, which substantially 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$\times$ 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.
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