Conference proceeding
A RUGD Dataset for Autonomous Navigation and Visual Perception in Unstructured Outdoor Environments
2019 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS), pp 5000-5007
01 Jan 2019
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Research in autonomous driving has benefited from a number of visual datasets collected from mobile platforms, leading to improved visual perception, greater scene understanding, and ultimately higher intelligence. However, this set of existing data collectively represents only highly structured, urban environments. Operation in unstructured environments, e.g., humanitarian assistance and disaster relief or off-road navigation, bears little resemblance to these existing data. To address this gap, we introduce the Robot Unstructured Ground Driving (RUGD) dataset with video sequences captured from a small, unmanned mobile robot traversing in unstructured environments. Most notably, this data differs from existing autonomous driving benchmark data in that it contains significantly more terrain types, irregular class boundaries, minimal structured markings, and presents challenging visual properties often experienced in off road navigation, e.g., blurred frames. Over 7; 000 frames of pixel-wise annotation are included with this dataset, and we perform an initial benchmark using state-of-the-art semantic segmentation architectures to demonstrate the unique challenges this data introduces as it relates to navigation tasks.
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Details
- Title
- A RUGD Dataset for Autonomous Navigation and Visual Perception in Unstructured Outdoor Environments
- Creators
- Maggie Wigness - DEVCOM Army Research LaboratorySungmin Eum - DEVCOM Army Research LaboratoryJohn G. Rogers - DEVCOM Army Research LaboratoryDavid Han - DEVCOM Army Research LaboratoryHeesung Kwon - DEVCOM Army Research Laboratory
- Publication Details
- 2019 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS), pp 5000-5007
- Series
- IEEE International Conference on Intelligent Robots and Systems
- Publisher
- IEEE
- Number of pages
- 8
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Web of Science ID
- WOS:000544658404015
- Scopus ID
- 2-s2.0-85081157332
- Other Identifier
- 991021931082204721
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InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Information Systems
- Computer Science, Theory & Methods
- Robotics