具有隐私和通信约束的分布式映射：轻量级算法和基于对象的模型
Distributed Mapping with Privacy and Communication Constraints: Lightweight Algorithms and Object-based Models
摘要：
我们考虑以下问题：一组机器人被部署在一个未知的环境中，它必须在没有可靠的通信基础设施的情况下协作构建该地区的地图。现代地图技术的支柱是姿态图优化，它估计机器人的轨迹，从中可以很容易地构建地图。本文的第一个贡献是一组分布式算法的姿态图优化：而不是发送所有的传感器数据到一个远程传感器融合服务器，机器人交换非常部分和嘈杂的信息，以达成协议的姿态图配置。我们的方法可以被认为是Carlone等人的两阶段方法的分布式实现，其中，我们使用连续超松弛（SOR）和雅可比超松弛（JOR）作为主力，在机器人之间分配计算。作为第二个贡献，我们扩展%，并证明了所提出的分布式算法与基于对象的地图模型的适用性。基于对象的模型的使用避免了原始传感器测量的交换（例如，点云），进一步减少通信负担。我们的第三个贡献是广泛的实验评估所提出的技术，包括测试在现实的露台模拟和现场实验在军事测试设施。大量的实验证据表明，所提出的算法之一（分布式高斯-赛德尔方法或DGS）具有优异的性能。DGS需要最少的信息交换，具有随时可用的特性，可以很好地扩展到大型团队，对噪声具有鲁棒性，并且易于实现。我们的现场测试表明，我们的分布式算法和基于对象的模型的结合使用减少了几个数量级的通信要求，并使分布式映射与大型团队的机器人在现实世界中的问题。
英文摘要：
We consider the following problem: a team of robots is deployed in an unknown environment and it has to collaboratively build a map of the area without a reliable infrastructure for communication. The backbone for modern mapping techniques is pose graph optimization, which estimates the trajectory of the robots, from which the map can be easily built. The first contribution of this paper is a set of distributed algorithms for pose graph optimization: rather than sending all sensor data to a remote sensor fusion server, the robots exchange very partial and noisy information to reach an agreement on the pose graph configuration. Our approach can be considered as a distributed implementation of the two-stage approach of Carlone et al., where we use the Successive Over-Relaxation (SOR) and the Jacobi Over-Relaxation (JOR) as workhorses to split the computation among the robots. As a second contribution, we extend %and demonstrate the applicability of the proposed distributed algorithms to work with object-based map models. The use of object-based models avoids the exchange of raw sensor measurements (e.g., point clouds) further reducing the communication burden. Our third contribution is an extensive experimental evaluation of the proposed techniques, including tests in realistic Gazebo simulations and field experiments in a military test facility. Abundant experimental evidence suggests that one of the proposed algorithms (the Distributed Gauss-Seidel method or DGS) has excellent performance. The DGS requires minimal information exchange, has an anytime flavor, scales well to large teams, is robust to noise, and is easy to implement. Our field tests show that the combined use of our distributed algorithms and object-based models reduces the communication requirements by several orders of magnitude and enables distributed mapping with large teams of robots in real-world problems.
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