DRACo-SLAM: Distributed Robust Acoustic Communication-efficient SLAM for Imaging Sonar Equipped Underwater Robot Teams
DRACo-SLAM: Distributed Robust Acoustic Communication-efficient SLAM for Imaging Sonar Equipped Underwater Robot Teams
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DOI：
10.1109/iros47612.2022.9981822
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发表时间：
2022-10
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影响因子：
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通讯作者：
J. McConnell;Yewei Huang;Paul Szenher;Ivana Collado-Gonzalez;Brendan Englot
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作者：
J. McConnell;Yewei Huang;Paul Szenher;Ivana Collado-Gonzalez;Brendan Englot
多机器人系统的一个基本任务是生成对环境和机器人之间的相对位姿的共同理解。只有当车辆知道自己的状态和团队成员的状态时，才能执行合作任务。然而，这主要是通过水下机器人之间的直接会合，通过机器人间测距来实现的。我们提出了一种新型的分布式多机器人同时定位和地图绘制（SLAM）框架，用于使用基于成像声纳的感知的水下机器人。通过在机器人之间仅传递场景描述符，我们不需要传递原始传感器数据，除非存在机器人间环路闭合的可能性。我们利用成对一致的测量集最大化（PCM），使我们的系统鲁棒性错误的循环关闭。我们的系统的功能是使用两个真实世界的数据集，一个有三个机器人，另一个有两个机器人。我们表明，我们的系统有效地估计多机器人系统的轨迹，并保持机器人间通信的带宽要求低。据我们所知，本文介绍了多机器人SLAM使用真实的成像声纳数据（我们实现离线，使用模拟通信）的第一个实例。代码链接：https://github.com/jake3991/DRACo-SLAM.
An essential task for a multi-robot system is generating a common understanding of the environment and relative poses between robots. Cooperative tasks can be executed only when a vehicle has knowledge of its own state and the states of the team members. However, this has primarily been achieved with direct rendezvous between underwater robots, via inter-robot ranging. We propose a novel distributed multi-robot simultaneous localization and mapping (SLAM) framework for underwater robots using imaging sonar-based perception. By passing only scene descriptors between robots, we do not need to pass raw sensor data unless there is a likelihood of inter-robot loop closure. We utilize pairwise consistent measurement set maximization (PCM), making our system robust to erroneous loop closures. The functionality of our system is demonstrated using two real-world datasets, one with three robots and another with two robots. We show that our system effectively estimates the trajectories of the multi-robot system and keeps the bandwidth requirements of inter-robot communication low. To our knowledge, this paper describes the first instance of multi-robot SLAM using real imaging sonar data (which we implement offline, using simulated communication). Code link: https://github.com/jake3991/DRACo-SLAM.