Hypermap Mapping Framework and its Application to Autonomous Semantic Exploration

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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.

Original languageEnglish
Title of host publicationProceedings of the 2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2020
PublisherIEEE
Pages133-139
Number of pages7
ISBN (Electronic)9781728164229
DOIs
Publication statusPublished - 14 Sep 2020
MoE publication typeA4 Article in a conference publication
EventIEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems - Virtual, Online
Duration: 14 Sep 202016 Sep 2020

Conference

ConferenceIEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems
Abbreviated titleMFI
CityVirtual, Online
Period14/09/202016/09/2020
OtherVirtual conference

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