Abstract
Simultaneous localization and mapping (SLAM) is a critical capability in autonomous navigation, but memory and computational limits make long-term application of common SLAM techniques impractical; a robot must be able to determine what information should be retained and what can safely be forgotten. In graph-based SLAM, the number of edges (measurements) in a pose graph determines both the memory requirements of storing a robot's observations and the computational expense of algorithms deployed for performing state estimation using those observations, both of which can grow unbounded during long-term navigation. Motivated by these challenges, we propose a new general purpose approach to sparsify graphs in a manner that maximizes algebraic connectivity, a key spectral property of graphs which has been shown to control the estimation error of pose graph SLAM solutions. Our algorithm, MAC (for maximizing algebraic connectivity), is simple and computationally inexpensive, and admits formal post hoc performance guarantees on the quality of the solution that it provides. In application to the problem of pose-graph SLAM, we show on several benchmark datasets that our approach quickly produces high-quality sparsification results which retain the connectivity of the graph and, in turn, the quality of corresponding SLAM solutions.
Abstract (translated)
同时定位与映射（SLAM）是自动驾驶中的关键能力，但内存和计算能力的限制使得长期应用常见的SLAM技术变得不可行；机器人必须能够确定应该保留哪些信息以及可以安全忘记哪些信息。在基于图的SLAM中，姿态图中的边数（测量）决定了存储机器人观测信息的内存需求以及使用这些观测信息进行状态估计的算法的计算开销，两者在长期导航过程中都可能无限制增长。为了应对这些挑战，我们提出了一种新的通用方法来稀疏图，以最大程度地增加图的 algebraic 连接性，这是图的一个重要特征，已被证明可以控制姿态图SLAM解决方案的估计误差。我们的算法MAC（用于最大化 algebraic connectivity）简单且计算成本低，并且对所提供解决方案的质量具有正式的后续性能保证。在应用到姿态图SLAM问题中，我们在多个基准数据集上证明了我们的方法可以快速产生高质量稀疏化结果，保留图的连接性，并进而保留相应的SLAM解决方案的质量。
URL
https://arxiv.org/abs/2403.19879
