Near-Optimal Budgeted Data Exchange for Distributed Loop Closure Detection
Near-Optimal Budgeted Data Exchange for Distributed Loop Closure Detection
复制标题
用于分布式环路闭合检测的近乎最优预算数据交换
DOI：
10.15607/rss.2018.xiv.071
复制
发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Jonathan Kelly
中科院分区：
文献类型：
--
作者：
Yulun Tian;Kasra Khosoussi;Matthew Giamou;J. How;Jonathan Kelly
机器人间环路闭合检测是协同SLAM（CLAM）的核心问题。建立机器人间环路闭合是一个需要资源的过程，在此过程中，机器人必须消耗大量的关键任务资源（例如，电池和带宽）来交换传感数据。然而，即使使用最具资源效率的技术，机载可用的资源也可能不足以验证每个潜在的环路闭合。这项工作解决了这个关键的挑战，提出了一个资源自适应的分布式循环闭合检测框架。我们寻求最大限度地提高以任务为导向的目标受到预算约束的总数据传输。这个问题一般是NP难的。我们从不同的角度来处理这个问题，并利用现有的结果单调子模最大化提供有效的近似算法的性能保证。所提出的方法进行了广泛的评估，使用KITTI里程计基准数据集和合成曼哈顿样数据集。
Inter-robot loop closure detection is a core problem in collaborative SLAM (CSLAM). Establishing inter-robot loop closures is a resource-demanding process, during which robots must consume a substantial amount of mission-critical resources (e.g., battery and bandwidth) to exchange sensory data. However, even with the most resource-efficient techniques, the resources available onboard may be insufficient for verifying every potential loop closure. This work addresses this critical challenge by proposing a resource-adaptive framework for distributed loop closure detection. We seek to maximize task-oriented objectives subject to a budget constraint on total data transmission. This problem is in general NP-hard. We approach this problem from different perspectives and leverage existing results on monotone submodular maximization to provide efficient approximation algorithms with performance guarantees. The proposed approach is extensively evaluated using the KITTI odometry benchmark dataset and synthetic Manhattan-like datasets.
影响因子：
7.8
作者：
Mur-Artal, Raul;Tardos, Juan D.
通讯作者：
Tardos, Juan D.