Hypermap Mapping Framework and its Application to Autonomous Semantic Exploration

TL;DR

The paper introduces Hypermap, a flexible framework for managing multiple map types in autonomous robots, and demonstrates its application in semantic exploration using RGB-D images.

Contribution

We present Hypermap, a versatile framework capable of handling various map layers, and an algorithm to generate semantic layers from RGB-D images for autonomous exploration.

Findings

Hypermap effectively manages multiple map types simultaneously.

The semantic layer generation algorithm accurately labels objects from RGB-D data.

Application to autonomous exploration improves environment understanding.

Abstract

Modern intelligent and autonomous robotic applications often require robots to have more information about their environment than that provided by traditional occupancy grid maps. For example, a robot tasked to perform autonomous semantic exploration has to label objects in the environment it is traversing while autonomously navigating. To solve this task the robot needs to at least maintain an occupancy map of the environment for navigation, an exploration map keeping track of which areas have already been visited, and a semantic map where locations and labels of objects in the environment are recorded. As the number of maps required grows, an application has to know and handle different map representations, which can be a burden. We present the Hypermap framework, which can manage multiple maps of different types. In this work, we explore the capabilities of the framework to handle occupancy grid layers and semantic polygonal layers, but the framework can be extended with new layer types in the future. Additionally, we present an algorithm to automatically generate semantic layers from RGB-D images. We demonstrate the utility of the framework using the example of autonomous exploration for semantic mapping.