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 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 in practice.

中文翻译：

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一种具有可证明性能保证的协作闭环检测的资源感知方法

本文介绍了用于分布式机器人间闭环检测的资源感知算法，适用于协作同时定位和映射 (CSLAM) 和分布式图像检索等应用。在现实世界中，这个过程是资源密集型的，因为它涉及交换许多观察结果和几何验证大量潜在匹配。这对具有各种操作和资源约束的小尺寸和低成本机器人提出了严峻挑战，这些约束限制了例如能源消耗、通信带宽和计算能力。本文提出了一个框架，其中机器人首先交换紧凑查询以识别一组潜在的闭环。然后，我们寻求为几何验证选择潜在的机器人间闭环的子集，在不超过计算（几何验证数量）和通信（用于几何验证的交换数据量）预算的情况下最大化单调子模块性能指标。我们证明了这个问题通常是 NP-hard 问题，并提出了具有可证明性能保证的有效逼近算法。所提出的框架在真实和合成数据集上得到了广泛的评估。还提出了一个自然的凸松弛方案，以证明所提出的框架在实践中接近最佳性能。我们证明了这个问题通常是 NP-hard 问题，并提出了具有可证明性能保证的有效逼近算法。所提出的框架在真实和合成数据集上得到了广泛的评估。还提出了一个自然的凸松弛方案，以证明所提出的框架在实践中接近最佳性能。我们证明了这个问题通常是 NP-hard 问题，并提出了具有可证明性能保证的有效逼近算法。所提出的框架在真实和合成数据集上得到了广泛的评估。还提出了一个自然的凸松弛方案，以证明所提出的框架在实践中接近最佳性能。