Active Semantic Mapping and Pose Graph Spectral Analysis for Robot Exploration

TL;DR

This paper presents an active semantic mapping approach that integrates spectral graph theory and information metrics to improve robot exploration, resulting in enhanced SLAM performance and semantic accuracy.

Contribution

It introduces a novel active metric-semantic SLAM method combining semantic mutual information and pose graph connectivity for better exploration strategies.

Findings

21% reduction in average map error

9% improvement in semantic classification accuracy

Maintains efficiency close to state-of-the-art methods

Abstract

Exploration in unknown and unstructured environments is a pivotal requirement for robotic applications. A robot's exploration behavior can be inherently affected by the performance of its Simultaneous Localization and Mapping (SLAM) subsystem, although SLAM and exploration are generally studied separately. In this paper, we formulate exploration as an active mapping problem and extend it with semantic information. We introduce a novel active metric-semantic SLAM approach, leveraging recent research advances in information theory and spectral graph theory: we combine semantic mutual information and the connectivity metrics of the underlying pose graph of the SLAM subsystem. We use the resulting utility function to evaluate different trajectories to select the most favorable strategy during exploration. Exploration and SLAM metrics are analyzed in experiments. Running our algorithm on the Habitat dataset, we show that, while maintaining efficiency close to the state-of-the-art exploration methods, our approach effectively increases the performance of metric-semantic SLAM with a 21% reduction in average map error and a 9% improvement in average semantic classification accuracy.