[Paper Reading] Scalable Distance-based Multi-Agent Relative State Estimation via Block Multiconvex Optimization
Scalable Distance-based Multi-Agent Relative State Estimation via Block Multiconvex Optimization [WGWF24] RSS 2024
Author: Tianyue Wu, Zaitian Gongye, Qianhao Wang, and Fei Gao
针对的问题：
- 从一系列距离测量中进行
相对位置估计 - 由此建模得到的数学模型是
高维且非凸
提出的方法：
- 建模：generalized graph realization
- introduce two collaborative optimization models, one of which is convex and thus globally solvable, and the other enables fast searching on non-convex landscapes to refine the solution offered by the convex one. Importantly, both models enjoy multiconvex and decompos able structures, allowing efficient and safe solutions using block coordinate descent that enjoys scalability and a distributed nature.
取得的效果：
- 计算精度超越或等同于当前的集中式的基于松弛的凸优化方法
- 可扩展性强于集中式的基于松弛的凸优化方法
- 在连续系统场景下，该方法优于局部搜索方法