Abstract
We present DRACo-SLAM2, a distributed SLAM framework for underwater robot teams equipped with multibeam imaging sonar. This framework improves upon the original DRACo-SLAM by introducing a novel representation of sonar maps as object graphs and utilizing object graph matching to achieve time-efficient inter-robot loop closure detection without relying on prior geometric information. To better-accommodate the needs and characteristics of underwater scan matching, we propose incremental Group-wise Consistent Measurement Set Maximization (GCM), a modification of Pairwise Consistent Measurement Set Maximization (PCM), which effectively handles scenarios where nearby inter-robot loop closures share similar registration errors. The proposed approach is validated through extensive comparative analyses on simulated and real-world datasets.
Abstract (translated)
我们介绍了DRACo-SLAM2，这是一个为装备有多波束成像声纳的水下机器人团队设计的分布式SLAM框架。该框架在原有DRACo-SLAM的基础上进行了改进，通过引入一种将声纳地图表示为对象图的新方法，并利用对象图匹配技术来实现高效的时间内机器人间的闭环检测，而无需依赖先验几何信息。为了更好地适应水下扫描匹配的需求和特性，我们提出了一种增量式的群组一致测量集最大化（GCM）算法，这是对成对一致测量集最大化的改进版本（PCM），能够有效处理附近机器人之间闭环共享类似注册误差的情况。通过在模拟数据和真实世界数据上的广泛对比分析验证了所提出的这种方法的有效性。
URL
https://arxiv.org/abs/2507.23629
