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The HDIN Dataset: A Real-World Indoor UAV Dataset with Multi-Task Labels for Visual-Based Navigation

Chang, Yingxiu, Cheng, Yongqiang, Murray, John, Huang, Shi and Shi, Guangyi (2022) The HDIN Dataset: A Real-World Indoor UAV Dataset with Multi-Task Labels for Visual-Based Navigation. Drones, 6 (8). ISSN 2504-446X

Item Type: Article


Supervised learning for Unmanned Aerial Vehicle (UAVs) visual-based navigation raises the need for reliable datasets with multi-task labels (e.g., classification and regression labels). However, current public datasets have limitations: (a) Outdoor datasets have limited generalization capability when being used to train indoor navigation models; (b) The range of multi-task labels, especially for regression tasks, are in different units which require additional transformation. In this paper, we present a Hull Drone Indoor Navigation (HDIN) dataset to improve the generalization capability for indoor visual-based navigation. Data were collected from the onboard sensors of a UAV. The scaling factor labeling method with three label types has been proposed to overcome the data jitters during collection and unidentical units of regression labels simultaneously. An open-source Convolutional Neural Network (i.e., DroNet) was employed as a baseline algorithm to retrain the proposed HDIN dataset, and compared with DroNet’s pretrained results on its original dataset since we have a similar data format and structure to the DroNet dataset. The results show that the labels in our dataset are reliable and consistent with the image samples.

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Uncontrolled Keywords: supervised learning; indoor visual-based navigation; real-world UAV dataset; multi-task labels; convolutional neural network (CNN); scaling factor labeling
Depositing User: Yongqiang Cheng


Item ID: 16821
Identification Number:
ISSN: 2504-446X
Official URL:

Users with ORCIDS

ORCID for Yongqiang Cheng: ORCID iD
ORCID for John Murray: ORCID iD

Catalogue record

Date Deposited: 20 Nov 2023 15:17
Last Modified: 20 Nov 2023 15:30


Author: Yongqiang Cheng ORCID iD
Author: John Murray ORCID iD
Author: Yingxiu Chang
Author: Shi Huang
Author: Guangyi Shi

University Divisions

Faculty of Technology > School of Computer Science



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