A resource-aware approach to collaborative loop-closure detection with provable performance guarantees
A resource-aware approach to collaborative loop-closure detection with provable performance guarantees
复制标题
具有可证明性能保证的资源感知协作闭环检测方法
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
J. How
中科院分区：
文献类型：
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作者：
Yulun Tian;Kasra Khosoussi;J. How
本文提出了资源感知算法的分布式机器人间闭环检测的应用，如协同同步定位和地图（CSLAM）和分布式图像检索。在现实世界的场景中，这个过程是资源密集型的，因为它涉及交换许多观察和几何验证大量的潜在匹配。这对小尺寸和低成本机器人提出了严峻的挑战，这些机器人具有各种操作和资源约束，能耗、通信带宽和计算能力。本文提出了一个框架，机器人首先交换紧凑的查询，以确定一组潜在的循环闭包。然后，我们试图选择一个子集的潜在的机器人间的循环关闭几何验证，最大限度地提高单调子模块化的性能指标，而不超过预算的计算（几何验证的数量）和通信（几何验证的交换数据量）。我们证明，这个问题是，在一般情况下，NP-难，并提出有效的近似算法，可证明的先验性能保证。所提出的框架进行了广泛的评估真实的和合成数据集。一个自然的凸松弛计划，也证明了近最优性能的后验建议的框架。
This paper presents resource-aware algorithms for distributed inter-robot loop-closure detection for applications such as collaborative simultaneous localization and mapping (CSLAM) and distributed image retrieval. In real-world scenarios, this process is resource-intensive as it involves exchanging many observations and geometrically verifying a large number of potential matches. This poses severe challenges for small-size and low-cost robots with various operational and resource constraints that limit, e.g., energy consumption, communication bandwidth, and computation capacity. This paper proposes a framework in which robots first exchange compact queries to identify a set of potential loop closures. We then seek to select a subset of potential inter-robot loop closures for geometric verification that maximizes a monotone submodular performance metric without exceeding budgets on computation (number of geometric verifications) and communication (amount of exchanged data for geometric verification). We demonstrate that this problem is, in general, NP-hard, and present efficient approximation algorithms with provable a priori performance guarantees. The proposed framework is extensively evaluated on real and synthetic datasets. A natural convex relaxation scheme is also presented to certify the near-optimal performance of the proposed framework a posteriori.