Learning to see the wood for the trees: Deep laser localization in urban and natural environments on a CPU

Localization in challenging, natural environments, such as forests or woodlands, is an important capability for many applications from guiding a robot navigating along a forest trail to monitoring vegetation growth with handheld sensors. In this letter, we explore laser-based localization in both ur...

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Bibliographic Details
Main Authors: Fallon, M, Tinchev, G, Penate-Sanchez, A
Format: Conference item
Published: Institute of Electrical and Electronics Engineers 2019
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author Fallon, M
Tinchev, G
Penate-Sanchez, A
author_facet Fallon, M
Tinchev, G
Penate-Sanchez, A
author_sort Fallon, M
collection OXFORD
description Localization in challenging, natural environments, such as forests or woodlands, is an important capability for many applications from guiding a robot navigating along a forest trail to monitoring vegetation growth with handheld sensors. In this letter, we explore laser-based localization in both urban and natural environments, which is suitable for online applications. We propose a deep learning approach capable of learning meaningful descriptors directly from three-dimensional point clouds by comparing triplets (anchor, positive, and negative examples). The approach learns a feature space representation for a set of segmented point clouds that are matched between a current and previous observations. Our learning method is tailored toward loop closure detection resulting in a small model that can be deployed using only a CPU. The proposed learning method would allow the full pipeline to run on robots with limited computational payloads, such as drones, quadrupeds, or Unmanned Ground Vehicles (UGVs).
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spelling oxford-uuid:e181235e-f4e4-408d-8235-6899d922bf592022-03-27T09:54:55ZLearning to see the wood for the trees: Deep laser localization in urban and natural environments on a CPUConference itemhttp://purl.org/coar/resource_type/c_5794uuid:e181235e-f4e4-408d-8235-6899d922bf59Symplectic Elements at OxfordInstitute of Electrical and Electronics Engineers2019Fallon, MTinchev, GPenate-Sanchez, ALocalization in challenging, natural environments, such as forests or woodlands, is an important capability for many applications from guiding a robot navigating along a forest trail to monitoring vegetation growth with handheld sensors. In this letter, we explore laser-based localization in both urban and natural environments, which is suitable for online applications. We propose a deep learning approach capable of learning meaningful descriptors directly from three-dimensional point clouds by comparing triplets (anchor, positive, and negative examples). The approach learns a feature space representation for a set of segmented point clouds that are matched between a current and previous observations. Our learning method is tailored toward loop closure detection resulting in a small model that can be deployed using only a CPU. The proposed learning method would allow the full pipeline to run on robots with limited computational payloads, such as drones, quadrupeds, or Unmanned Ground Vehicles (UGVs).
spellingShingle Fallon, M
Tinchev, G
Penate-Sanchez, A
Learning to see the wood for the trees: Deep laser localization in urban and natural environments on a CPU
title Learning to see the wood for the trees: Deep laser localization in urban and natural environments on a CPU
title_full Learning to see the wood for the trees: Deep laser localization in urban and natural environments on a CPU
title_fullStr Learning to see the wood for the trees: Deep laser localization in urban and natural environments on a CPU
title_full_unstemmed Learning to see the wood for the trees: Deep laser localization in urban and natural environments on a CPU
title_short Learning to see the wood for the trees: Deep laser localization in urban and natural environments on a CPU
title_sort learning to see the wood for the trees deep laser localization in urban and natural environments on a cpu
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