マルチレベル分割を用いた分散協調SLAMのためのポーズグラフ最適化
Distributed Pose-graph Optimization with Multi-level Partitioning for Collaborative SLAM
分散協調SLAMのバックエンドで用いられるポーズグラフ最適化に対し、マルチレベルグラフ分割と改良リーマン・ブロック座標降下法を組み合わせ、通信負荷を抑えつつ大域的最適解を得る手法を提案した。
著者: Cunhao Li, Peng Yi, Guanghui Guo, Yiguang Hong
分類: cs.RO, cs.MA
原文アブストラクト
The back-end module of Distributed Collaborative Simultaneous Localization and Mapping (DCSLAM) requires solving a nonlinear Pose Graph Optimization (PGO) under a distributed setting, also known as SE(d)-synchronization. Most existing distributed graph optimization algorithms employ a simple sequential partitioning scheme, which may result in unbalanced subgraph dimensions due to the different geographic locations of each robot, and hence imposes extra communication load. Moreover, the performance of current Riemannian optimization algorithms can be further accelerated. In this letter, we propose a novel distributed pose graph optimization algorithm combining multi-level partitioning with an accelerated Riemannian optimization method. Firstly, we employ the multi-level graph partitioning algorithm to preprocess the naive pose graph to formulate a balanced optimization problem. In addition, inspired by the accelerated coordinate descent method, we devise an Improved Riemannian Block Coordinate Descent (IRBCD) algorithm and the critical point obtained is globally optimal. Finally, we evaluate the effects of four common graph partitioning approaches on the correlation of the inter-subgraphs, and discover that the Highest scheme has the best partitioning performance. Also, we implement simulations to quantitatively demonstrate that our proposed algorithm outperforms the state-of-the-art distributed pose graph optimization protocols.
