Data-Efficient Decentralized Visual SLAM

TL;DR

This paper presents a data-efficient decentralized visual SLAM system that reduces communication complexity from quadratic to linear with respect to robot count, enabling scalable multi-robot localization and mapping.

Contribution

It introduces a novel two-stage data association method using compact image descriptors, significantly decreasing data transfer requirements in decentralized SLAM.

Findings

Data association complexity scales linearly with robot count.

The system achieves efficient decentralized pose-graph optimization.

Open-source implementation is provided for benchmarking.

Abstract

Decentralized visual simultaneous localization and mapping (SLAM) is a powerful tool for multi-robot applications in environments where absolute positioning systems are not available. Being visual, it relies on cameras, cheap, lightweight and versatile sensors, and being decentralized, it does not rely on communication to a central ground station. In this work, we integrate state-of-the-art decentralized SLAM components into a new, complete decentralized visual SLAM system. To allow for data association and co-optimization, existing decentralized visual SLAM systems regularly exchange the full map data between all robots, incurring large data transfers at a complexity that scales quadratically with the robot count. In contrast, our method performs efficient data association in two stages: in the first stage a compact full-image descriptor is deterministically sent to only one robot. In the second stage, which is only executed if the first stage succeeded, the data required for relative pose estimation is sent, again to only one robot. Thus, data association scales linearly with the robot count and uses highly compact place representations. For optimization, a state-of-the-art decentralized pose-graph optimization method is used. It exchanges a minimum amount of data which is linear with trajectory overlap. We characterize the resulting system and identify bottlenecks in its components. The system is evaluated on publicly available data and we provide open access to the code.