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Integrated design-sense-plan architecture for autonomous geometric-semantic mapping with UAVs
This article presents a complete solution for autonomous mapping and inspection tasks, namely a lightweight multi-camera drone design coupled with computationally efficient planning algorithms and environment representations for enhanced autonomous navigation in exploration and mapping tasks. The pr...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9493041/ https://www.ncbi.nlm.nih.gov/pubmed/36158605 http://dx.doi.org/10.3389/frobt.2022.911974 |
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author | Pimentel de Figueiredo, Rui Le Fevre Sejersen, Jonas Grimm Hansen, Jakob Brandão, Martim |
author_facet | Pimentel de Figueiredo, Rui Le Fevre Sejersen, Jonas Grimm Hansen, Jakob Brandão, Martim |
author_sort | Pimentel de Figueiredo, Rui |
collection | PubMed |
description | This article presents a complete solution for autonomous mapping and inspection tasks, namely a lightweight multi-camera drone design coupled with computationally efficient planning algorithms and environment representations for enhanced autonomous navigation in exploration and mapping tasks. The proposed system utilizes state-of-the-art Next-Best-View (NBV) planning techniques, with geometric and semantic segmentation information computed with Deep Convolutional Neural Networks (DCNNs) to improve the environment map representation. The main contributions of this article are the following. First, we propose a novel efficient sensor observation model and a utility function that encodes the expected information gains from observations taken from specific viewpoints. Second, we propose a reward function that incorporates both geometric and semantic probabilistic information provided by a DCNN for semantic segmentation that operates in close to real-time. The incorporation of semantics in the environment representation enables biasing exploration towards specific object categories while disregarding task-irrelevant ones during path planning. Experiments in both a virtual and a real scenario demonstrate the benefits on reconstruction accuracy of using semantics for biasing exploration towards task-relevant objects, when compared with purely geometric state-of-the-art methods. Finally, we present a unified approach for the selection of the number of cameras on a UAV, to optimize the balance between power consumption, flight-time duration, and exploration and mapping performance trade-offs. Unlike previous design optimization approaches, our method is couples with the sense and plan algorithms. The proposed system and general formulations can be be applied in the mapping, exploration, and inspection of any type of environment, as long as environment dependent semantic training data are available, with demonstrated successful applicability in the inspection of dry dock shipyard environments. |
format | Online Article Text |
id | pubmed-9493041 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-94930412022-09-23 Integrated design-sense-plan architecture for autonomous geometric-semantic mapping with UAVs Pimentel de Figueiredo, Rui Le Fevre Sejersen, Jonas Grimm Hansen, Jakob Brandão, Martim Front Robot AI Robotics and AI This article presents a complete solution for autonomous mapping and inspection tasks, namely a lightweight multi-camera drone design coupled with computationally efficient planning algorithms and environment representations for enhanced autonomous navigation in exploration and mapping tasks. The proposed system utilizes state-of-the-art Next-Best-View (NBV) planning techniques, with geometric and semantic segmentation information computed with Deep Convolutional Neural Networks (DCNNs) to improve the environment map representation. The main contributions of this article are the following. First, we propose a novel efficient sensor observation model and a utility function that encodes the expected information gains from observations taken from specific viewpoints. Second, we propose a reward function that incorporates both geometric and semantic probabilistic information provided by a DCNN for semantic segmentation that operates in close to real-time. The incorporation of semantics in the environment representation enables biasing exploration towards specific object categories while disregarding task-irrelevant ones during path planning. Experiments in both a virtual and a real scenario demonstrate the benefits on reconstruction accuracy of using semantics for biasing exploration towards task-relevant objects, when compared with purely geometric state-of-the-art methods. Finally, we present a unified approach for the selection of the number of cameras on a UAV, to optimize the balance between power consumption, flight-time duration, and exploration and mapping performance trade-offs. Unlike previous design optimization approaches, our method is couples with the sense and plan algorithms. The proposed system and general formulations can be be applied in the mapping, exploration, and inspection of any type of environment, as long as environment dependent semantic training data are available, with demonstrated successful applicability in the inspection of dry dock shipyard environments. Frontiers Media S.A. 2022-09-08 /pmc/articles/PMC9493041/ /pubmed/36158605 http://dx.doi.org/10.3389/frobt.2022.911974 Text en Copyright © 2022 Pimentel de Figueiredo, Le Fevre Sejersen, Grimm Hansen and Brandão. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Robotics and AI Pimentel de Figueiredo, Rui Le Fevre Sejersen, Jonas Grimm Hansen, Jakob Brandão, Martim Integrated design-sense-plan architecture for autonomous geometric-semantic mapping with UAVs |
title | Integrated design-sense-plan architecture for autonomous geometric-semantic mapping with UAVs |
title_full | Integrated design-sense-plan architecture for autonomous geometric-semantic mapping with UAVs |
title_fullStr | Integrated design-sense-plan architecture for autonomous geometric-semantic mapping with UAVs |
title_full_unstemmed | Integrated design-sense-plan architecture for autonomous geometric-semantic mapping with UAVs |
title_short | Integrated design-sense-plan architecture for autonomous geometric-semantic mapping with UAVs |
title_sort | integrated design-sense-plan architecture for autonomous geometric-semantic mapping with uavs |
topic | Robotics and AI |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9493041/ https://www.ncbi.nlm.nih.gov/pubmed/36158605 http://dx.doi.org/10.3389/frobt.2022.911974 |
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