Lighthouses and Global Graph Stabilization: Active SLAM for Low-compute, Narrow-FoV Robots

TL;DR

This paper introduces a novel SLAM exploration approach for low-compute, narrow-FOV robots, using lighthouse viewpoints and a global stabilization strategy to improve map quality and navigation stability in real-world environments.

Contribution

The paper proposes the concept of lighthouses and a global stabilization method, enhancing SLAM stability for resource-constrained robots with limited field-of-view.

Findings

Lighthouse views improve local map stability.

Global pose graph stabilization enhances overall map accuracy.

SAE strategy outperforms baseline exploration methods.

Abstract

Autonomous exploration to build a map of an unknown environment is a fundamental robotics problem. However, the quality of the map directly influences the quality of subsequent robot operation. Instability in a simultaneous localization and mapping (SLAM) system can lead to poorquality maps and subsequent navigation failures during or after exploration. This becomes particularly noticeable in consumer robotics, where compute budget and limited field-of-view are very common. In this work, we propose (i) the concept of lighthouses: panoramic views with high visual information content that can be used to maintain the stability of the map locally in their neighborhoods and (ii) the final stabilization strategy for global pose graph stabilization. We call our novel exploration strategy SLAM-aware exploration (SAE) and evaluate its performance on real-world home environments.