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TOMD: A Trail-based Off-road Multimodal Dataset for Traversable Pathway Segmentation under Challenging Illumination Conditions

We present TOMD, designed to capture complex, unstructured trail environments using a medium-scale all-terrain robot platform. The dataset integrates high-fidelity 3D LiDAR (128 channels), stereo imagery, GNSS, IMU, telemetry control data, and illumination measurements, all collected through repeated traversals under varying environmental conditions. Data was gathered in the hilly regions near the Department of Mathematics and Computer Science at Durham University (United Kingdom), encompassing diverse natural terrains such as grasslands, bushes, tree-dense areas, leaf-covered trails, and slopes.

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Resource type
Collection
Total Items
0
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Contributors
Creator: Sun, Yixin 1
Data collector: Sun, Yixin 1
Editor: Sun, Yixin 1
Creator: Breckon, Toby P. 1
Editor: Breckon, Toby P. 1
Editor: Li, Li 1
Editor: e, wenke 1
Creator: Atapour-Abarghouei, Amir 1
Editor: Atapour-Abarghouei, Amir 1
1 Durham university
Funder
Durham University
Research methods
Data collection performed in the UK, under Durham University Ethics
Keyword
Dataset
Off-road robotics
Robotic navigation
High resolution LiDAR
Stereo vision
Semantic segmentation
Subject
Artificial intelligence
Computer vision--Data processing
Computer Science
Robotics
Location
Durham, England, United Kingdom
Language
English
Identifier
ark:/32150/r2dn39x159k
Publisher
Durham University
Date Created
June, 2024
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