Robotic Exploration through Semantic Topometric Mapping

TL;DR

This paper presents a fast, efficient robotic exploration method using semantic topometric maps that incorporate structural environment information, improving exploration speed and reducing computational load in diverse environments.

Contribution

The paper introduces a novel semantic topometric mapping and exploration strategy that leverages structural semantics for improved efficiency and versatility in unknown environments.

Findings

Faster exploration compared to traditional methods

Reduced computation time during exploration

Effective in both indoor and outdoor environments

Abstract

In this article, we introduce a novel strategy for robotic exploration in unknown environments using a semantic topometric map. As it will be presented, the semantic topometric map is generated by segmenting the grid map of the currently explored parts of the environment into regions, such as intersections, pathways, dead-ends, and unexplored frontiers, which constitute the structural semantics of an environment. The proposed exploration strategy leverages metric information of the frontier, such as distance and angle to the frontier, similar to existing frameworks, with the key difference being the additional utilization of structural semantic information, such as properties of the intersections leading to frontiers. The algorithm for generating semantic topometric mapping utilized by the proposed method is lightweight, resulting in the method's online execution being both rapid and computationally efficient. Moreover, the proposed framework can be applied to both structured and unstructured indoor and outdoor environments, which enhances the versatility of the proposed exploration algorithm. We validate our exploration strategy and demonstrate the utility of structural semantics in exploration in two complex indoor environments by utilizing a Turtlebot3 as the robotic agent. Compared to traditional frontier-based methods, our findings indicate that the proposed approach leads to faster exploration and requires less computation time.