Resource-Aware Algorithms for Distributed Loop Closure Detection with Provable Performance Guarantees

TL;DR

This paper introduces resource-aware algorithms for distributed loop closure detection in multi-robot SLAM, optimizing detection under resource constraints with provable guarantees and near-optimal performance.

Contribution

It proposes novel approximation algorithms for resource-constrained loop closure detection with theoretical performance guarantees.

Findings

Algorithms achieve near-optimal performance in real and synthetic benchmarks.

Resource-aware approach reduces computational and communication costs.

Provable approximation guarantees for the proposed algorithms.

Abstract

Inter-robot loop closure detection, e.g., for collaborative simultaneous localization and mapping (CSLAM), is a fundamental capability for many multirobot applications in GPS-denied regimes. In real-world scenarios, this is a resource-intensive process that involves exchanging observations and verifying potential matches. This poses severe challenges especially 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 presents resource-aware algorithms for distributed inter-robot loop closure detection. In particular, we seek to select a subset of potential inter-robot loop closures that maximizes a monotone submodular performance metric without exceeding computation and communication budgets. We demonstrate that this problem is in general NP-hard, and present efficient approximation algorithms with provable performance guarantees. A convex relaxation scheme is used to certify near-optimal performance of the proposed framework in real and synthetic SLAM benchmarks.