Abstract
In this paper, we address the lack of datasets for - and the issue of reproducibility in - collaborative SLAM pose graph optimizers by providing a novel pose graph generator. Our pose graph generator, kollagen, is based on a random walk in a planar grid world, similar to the popular M3500 dataset for single agent SLAM. It is simple to use and the user can set several parameters, e.g., the number of agents, the number of nodes, loop closure generation probabilities, and standard deviations of the measurement noise. Furthermore, a qualitative execution time analysis of our pose graph generator showcases the speed of the generator in the tunable parameters. In addition to the pose graph generator, our paper provides two example datasets that researchers can use out-of-the-box to evaluate their algorithms. One of the datasets has 8 agents, each with 3500 nodes, and 67645 constraints in the pose graphs, while the other has 5 agents, each with 10000 nodes, and 76134 constraints. In addition, we show that current state-of-the-art pose graph optimizers are able to process our generated datasets and perform pose graph optimization. The data generator can be found at this https URL.
Abstract (translated)
在本文中,我们通过提供一个新的姿态图生成器来解决缺少 - 以及在协作式SLAM姿态图优化中重现性问题 - 的问题,同时也提供了两个可用于评估算法的示例数据集。我们的数据集生成器Kollagen基于平面网格世界的随机漫步,类似于单Agent SLAM中流行的M3500数据集。它易于使用,用户可设置多个参数,例如每个代理的数量、节点数、循环闭生成概率以及测量噪声的标准差。此外,我们对数据集生成器进行了定性执行时间分析,展示了生成器在可调整参数方面的速度和性能。除了数据集生成器外,我们提供了两个示例数据集,研究人员可以从中框中选择使用来评估算法。其中一个数据集包含8个代理,每个代理有3500个节点,67645个约束,另一个是5个代理,每个代理有10000个节点,76134个约束。此外,我们展示了当前最先进的姿态图优化器能够处理我们生成的数据集并进行姿态图优化。数据生成器可在本URL中找到。
URL
https://arxiv.org/abs/2303.04753
