水下机器人团队用声纳地图匹配实现高效定位，无需预设几何信息。
DRACo-SLAM2: Distributed Robust Acoustic Communication-efficient SLAM for Imaging Sonar EquippedUnderwater Robot Teams with Object Graph Matching
- 将声纳地图建模为物体图，通过图匹配实现快速跨机器人回环检测。
- 提出增量式分组一致性测量集最大化方法，解决邻近回环误差相似问题。
- 适用于多水下机器人协同导航，尤其适合复杂声学环境下的实时定位。
我们提出 DRACo-SLAM2，一种面向配备多波束成像声纳的水下机器人团队的分布式 SLAM 框架。该框架在原始 DRACo-SLAM 基础上引入了基于物体图的声纳地图表示，并利用物体图匹配实现无需依赖先验几何信息的时间高效跨机器人回环检测。为更好适应水下扫描匹配的需求，我们提出了增量式分组一致性测量集最大化（GCM），作为配对一致性测量集最大化（PCM）的改进，有效处理邻近机器人回环共享相似注册误差的情形。所提方法在模拟与真实数据集上进行了广泛对比验证。
原文摘要 · Abstract (English)
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.
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