An Efficient Global Optimality Certificate for Landmark-Based SLAM
An Efficient Global Optimality Certificate for Landmark-Based SLAM
复制标题
基于地标的 SLAM 的高效全局最优性证书
DOI：
--
复制
发表时间：
2022
影响因子：
5.2
通讯作者：
T. Barfoot
中科院分区：
文献类型：
--
作者：
Connor T. Holmes;T. Barfoot
现代状态估计通常被表述为一个优化问题，并使用高效的局部搜索方法来解决。这些方法最多保证收敛到局部最小值，但在某些情况下，也可以证明全局最优性。虽然这种全局最优性证明已经很好地建立了3D姿态图优化，但基于3D地标的SLAM问题的细节尚未制定出来，其中估计状态包括机器人姿态和地图地标。在这封信中，我们通过使用图论方法来解决这一差距，将基于地标的SLAM的子问题转换为产生全局最优性的充分条件的形式。目前存在计算这些子问题的最优性证书的有效方法，但首先需要构造一个大的数据矩阵。我们表明，该矩阵可以构建的复杂性，保持线性的地标数量，不超过一个局部求解器迭代的最先进的计算复杂性。我们证明了证书在模拟和现实世界中基于地标的SLAM问题上的有效性。我们还将我们的方法集成到最先进的SE-Sync管道中，以有效地解决基于地标的SLAM问题，达到全局最优。最后，考虑了底层测量图的影响，研究了全局最优性证明对测量噪声的鲁棒性。
Modern state estimation is often formulated as an optimization problem and solved using efficient local search methods. These methods at best guarantee convergence to local minima, but, in some cases, global optimality can also be certified. Although such global optimality certificates have been well established for 3D pose-graph optimization, the details have yet to be worked out for the 3D landmark-based SLAM problem, in which estimated states include both robot poses and map landmarks. In this letter, we address this gap by using a graph-theoretic approach to cast the subproblems of landmark-based SLAM into a form that yields a sufficient condition for global optimality. Efficient methods of computing the optimality certificates for these subproblems exist, but first require the construction of a large data matrix. We show that this matrix can be constructed with complexity that remains linear in the number of landmarks and does not exceed the state-of-the-art computational complexity of one local solver iteration. We demonstrate the efficacy of the certificate on simulated and real-world landmark-based SLAM problems. We also integrate our method into the state-of-the-art SE-Sync pipeline to efficiently solve landmark-based SLAM problems to global optimality. Finally, we study the robustness of the global optimality certificate to measurement noise, taking into consideration the effect of the underlying measurement graph.
影响因子：
7.8
作者：
Fan, Taosha;Wang, Hanlin;Murphey, Todd
通讯作者：
Murphey, Todd