Near-Optimal Budgeted Data Exchange for Distributed Loop Closure Detection

TL;DR

This paper presents a resource-adaptive framework for distributed loop closure detection in collaborative SLAM, optimizing data exchange under resource constraints with provable approximation guarantees, and validated on real and synthetic datasets.

Contribution

It introduces a novel approximation algorithm for resource-constrained loop closure detection, leveraging submodular optimization techniques with theoretical performance guarantees.

Findings

Achieves near-optimal data exchange efficiency under resource budgets.

Provides provable approximation guarantees for the optimization problem.

Demonstrates effectiveness on KITTI and synthetic datasets.

Abstract

Inter-robot loop closure detection is a core problem in collaborative SLAM (CSLAM). Establishing inter-robot loop closures is a resource-demanding process, during which robots must consume a substantial amount of mission-critical resources (e.g., battery and bandwidth) to exchange sensory data. However, even with the most resource-efficient techniques, the resources available onboard may be insufficient for verifying every potential loop closure. This work addresses this critical challenge by proposing a resource-adaptive framework for distributed loop closure detection. We seek to maximize task-oriented objectives subject to a budget constraint on total data transmission. This problem is in general NP-hard. We approach this problem from different perspectives and leverage existing results on monotone submodular maximization to provide efficient approximation algorithms with performance guarantees. The proposed approach is extensively evaluated using the KITTI odometry benchmark dataset and synthetic Manhattan-like datasets.