A resource-aware approach to collaborative loop-closure detection with provable performance guarantees. (September 2021)

• Record Type: Journal Article
• Authors: Tian, Yulun; Khosoussi, Kasra; How, Jonathan P
• Other Names: Morales Marco guest-editor.; Tapia Lydia guest-editor.; Sánchez-Ante Gildardo guest-editor.; Hutchinson Seth guest-editor.
• Is Part Of: International journal of robotics research. Volume 40:Number 10/11(2021)
• Page Start: 1212
• Page End: 1233
• Publication Date: 2021-09
• Subjects: Multi-robot SLAM -- distributed place recognition -- distributed loop closure detection -- resource-aware algorithms -- submodular optimization -- performance guarantees; Robots -- Periodicals; Robots, Industrial -- Periodicals; 629.89205
• DOI: 10.1177/0278364920948594
• Languages: English
• ISSNs: 0278-3649

Abstract

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.