University of Michigan North Campus long-term vision and lidar dataset

This paper documents a large scale, long-term autonomy dataset for robotics research collected on the University of Michigan’s North Campus. The dataset consists of omnidirectional imagery, 3D lidar, planar lidar, GPS, and proprioceptive sensors for odometry collected using a Segway robot. The datas...

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Bibliographic Details
Published in:The International journal of robotics research 2016-08, Vol.35 (9), p.1023-1035
Main Authors: Carlevaris-Bianco, Nicholas, Ushani, Arash K, Eustice, Ryan M
Format: Article
Language:English
Subjects:
Autonomous
Autonomy
Changing environments
Climatology
College campuses
Colleges & universities
Datasets
Falling
Focusing
Illumination
Imagery
Lidar
Lighting
Moving obstacles
Odometers
Pedestrians
Robotics
Robots
Seasons
Sensors
Snow
Trajectories
Universities
Weather
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Summary:This paper documents a large scale, long-term autonomy dataset for robotics research collected on the University of Michigan’s North Campus. The dataset consists of omnidirectional imagery, 3D lidar, planar lidar, GPS, and proprioceptive sensors for odometry collected using a Segway robot. The dataset was collected to facilitate research focusing on long-term autonomous operation in changing environments. The dataset is composed of 27 sessions spaced approximately biweekly over the course of 15 months. The sessions repeatedly explore the campus, both indoors and outdoors, on varying trajectories, and at different times of the day across all four seasons. This allows the dataset to capture many challenging elements including: moving obstacles (e.g. pedestrians, bicyclists and cars), changing lighting, varying viewpoint, seasonal and weather changes (e.g. falling leaves and snow), and long-term structural changes caused by construction projects. To further facilitate research, we also provide ground-truth pose for all sessions in a single frame of reference.
ISSN:0278-3649
1741-3176
DOI:10.1177/0278364915614638