具有可证明性能保证的协同循环闭包检测的资源感知方法

作者：Yulun Tian,Kasra Khosoussi,Jonathan P. How

摘要：本文介绍了用于分布式机器人闭环检测的资源感知算法，用于协同同步定位和映射（CSLAM）和分布式图像检索等应用。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并几何验证大量潜在的匹配。这对具有各种操作和资源限制的小尺寸和低成本机器人提出了严峻挑战，这限制了例如能量消耗，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循环闭包。然后，我们寻求选择用于几何验证的潜在机器人间闭环的子集，其最大化单调子模块性能度量，而不超过计算预算（几何验证的数量）和通信（用于几何验证的交换数据的量）。我们证明了这个问题通常是NP难的，并且提出了具有可证明的性能保证的有效近似算法。所提出的框架在实际和合成数据集上进行了广泛的评估。还提出了一种自然凸松弛方案，以证明所提出的框架在实践中的近乎最佳性能。

原文标题：A Resource-Aware Approach to Collaborative Loop Closure Detection with Provable Performance Guarantees

原文摘要：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.

地址：https://arxiv.org/abs/1907.04904