Abstract:
Note: If you want only a subset of the data presented here (just the ROS bags, but not the extracted JPEGs and CSVs for example, since there is some duplication), you can set your torrent client to download only the files you are interested in. License: This data is licensed under the Creative Commons - Attribution 4.0 International License: https://creativecommons.org/licenses/by/4.0. We present a novel long-term dataset of semistructured woodland terrain under varying lighting and weather conditions and with changing vegetation, infrastructure, and pedestrian traffic. This dataset is intended to aid the development of field robotics algorithms for long-term deployment in challenging outdoor environments. It includes more than 8 hours of trail navigation, with more available in the future as the environment changes. The data consist of readings from calibrated and synchronized sensors operating at 5 Hz to 50 Hz in the form of color stereo and grayscale monocular camera images, vertical and push-broom laser scans, GPS locations, wheel odometry, inertial measurements, and barometric pressure values. Each traversal covers approximately 4 km across three diverse woodland trail environments, and we have recorded under four different lighting and weather conditions to date: dry; wet; dusk; night. We also provide 383 hand-matched location correspondences between traversals as ground-truth for benchmarking place recognition and mapping algorithms. This paper describes the configuration of the vehicle, the trail environments covered, and the format of the data we provide.
Year: 2015
Booktitle: Workshop on Visual Place Recognition in Changing Environments at the IEEE International Conference on Robotics and Automation (ICRA'15 workshop), Seattle, WA, USA

URL: http://autonomylab.org/sfu-mountain-dataset
License: https://creativecommons.org/licenses/by/4.0

@InProceedings{bruce:icra15workshop,
author= {Jake Bruce and Jens Wawerla and Richard T. Vaughan},
title= {The SFU Mountain Dataset: Semi-Structured Woodland Trails Under Changing Environmental Conditions},
Booktitle= {Workshop on Visual Place Recognition in Changing Environments at the IEEE International Conference on Robotics and Automation (ICRA'15 workshop), Seattle, WA, USA},
month= {May},
year= {2015},
url= {http://autonomylab.org/sfu-mountain-dataset},
license= {https://creativecommons.org/licenses/by/4.0},
abstract= {Note: If you want only a subset of the data presented here (just the ROS bags, but not the extracted JPEGs and CSVs for example, since there is some duplication), you can set your torrent client to download only the files you are interested in.

License: This data is licensed under the Creative Commons - Attribution 4.0 International License: https://creativecommons.org/licenses/by/4.0.

We present a novel long-term dataset of semistructured woodland terrain under varying lighting and weather conditions and with changing vegetation, infrastructure, and pedestrian traffic. This dataset is intended to aid the development of field robotics algorithms for long-term deployment in challenging outdoor environments. It includes more than 8 hours of trail navigation, with more available in the future as the environment changes. The data consist of readings from calibrated and synchronized sensors operating at 5 Hz to 50 Hz in the form of color stereo and grayscale monocular camera images, vertical and push-broom laser scans, GPS locations, wheel odometry, inertial measurements, and barometric pressure values. Each traversal covers approximately 4 km across three diverse woodland trail environments, and we have recorded under four different lighting and weather conditions to date: dry; wet; dusk; night. We also provide 383 hand-matched location correspondences between traversals as ground-truth for benchmarking place recognition and mapping algorithms. This paper describes the configuration of the vehicle, the trail environments covered, and the format of the data we provide.},
keywords= {Computer Science, robotics, Burnaby Mountain, Trans-Canada Trail, powerline trail, forest trails, video, lidar, imu, gps, barometric pressure, stereo and monocular cameras},
terms= {}
}