A hybrid representation of the environment to improve autonomous navigation of mobile robots in agriculture
Type de document
journalArticle
Langue source
Anglais
Titre français
Titre anglais
A hybrid representation of the environment to improve autonomous navigation of mobile robots in agriculture
Auteur(s)
- EMMI L.
- LE FLÉCHER E.
- CADENAT V.
- DEVY M.
Editeur(s)
Autre(s)
Id
VG29GVHL
Version
2343
Date ajout
15 janvier 2021 23:33
Date modification
15 janvier 2021 23:33
Résumé anglais
This paper considers the problem of autonomous navigation in agricultural fields. It proposes a localization and mapping framework based on semantic place classification and key location estimation, which together build a hybrid topological map. This map benefits from generic partitioning of the field, which contains a finite set of well-differentiated workspaces and, through a semantic analysis, it is possible to estimate in a probabilistic way the position (state) of a mobile system in the field. Moreover, this map integrates both metric (key locations) and semantic features (working areas). One of its advantages is that a full and precise map prior to navigation is not necessary. The identification of the key locations and working areas is carried out by a perception system based on 2D LIDAR and RGB cameras. Fusing these data with odometry allows the robot to be located in the topological map. The approach is assessed through off-line data recorded in real conditions in diverse fields during different seasons. It exploits a real-time object detector based on a convolutional neural network called you only look once, version 3, which has been trained to classify a considerable number of crops, including market-garden crops such as broccoli and cabbage, and to identify grapevine trunks. The results show the interest in the approach, which allows (i) obtaining a simple and easy-to-update map, (ii) avoiding the use of artificial landmarks, and thus (iii) improving the autonomy of agricultural robots.
Note
None
CRAW tags
- AB - Transversal
- FREDO technologie et innovation
- GEO France
- machine learning
- robot
WEB tags
Titre de la publication
Precision Agriculture
Date caractères
2021-01-02
Date publication
2 janvier 2021
Doi
10.1007/s11119-020-09773-9
Le DOI est une URL unique de référencement d'une publication.
Il est donc plus fiable et permanent qu'une URL classique
Issn
1573-1618
L’ISSN est un code de 8 chiffres servant à identifier
les journaux, revues, magazines, périodiques de toute nature et
sur tous supports, papier comme électronique.
Url publication