FinnForest : A Forest Landscape for Visual SLAM

FinnForest : A Forest Landscape for Visual SLAM

Description

## Article Abstract ------------ We provide a novel challenging dataset that offers a new landscape of testing material for mobile robotics, autonomous driving research, and heavy machine operation. In contrast to common urban structures, we explore an unregulated natural environment to exemplify sub-urban and forest environment. The sequences provide two-natured data where each place is visited in summer and winter conditions. The vehicle used for recording is equipped with a sensor rig that constitutes four RGB cameras, an Inertial Measurement Unit, and a Global Navigation Satellite System receiver. The sensors are synchronized based on non-drifting timestamps. The dataset provides trajectories of varying complexity both for the state of the art visual odometry approaches and visual simultaneous localization and mapping algorithms. ## Suggested Citation ------------ @article{ali_durmush_suominen_yli-hietanen_peltonen_collin_gotchev_2020, title= {FinnForest dataset: A forest landscape for visual SLAM}, volume= {132}, DOI={10.1016/j.robot.2020.103610}, journal= {Robotics and Autonomous Systems}, author= {Ali, Ihtisham and Durmush, Ahmed and Suominen, Olli and Yli-Hietanen, Jari and Peltonen, Sari and Collin, Jussi and Gotchev, Atanas}, year= {2020}, pages= {103610} ## Introductory Video ------------ [![alt text](https://github.com/ihtishamaliktk/finnforest/blob/master/figures/videoicon.png?raw=true)](https://youtu.be/bGyEf3zUj-w "Introduction video") ## Dataset Directory ------------ ![alt text](https://github.com/ihtishamaliktk/finnforest/blob/master/figures/DatasetDirectory.png?raw=true) ## Guiding Points ------------ - To start using directly, you can download individual sequences from the RectifiedData in the form of Images or Rosbags e.g. *rectifiedImageFormat* > *dataset_40Hz* > *'desiredSequence'.zip* - The ground truth of the trajectories and calibration information of the cameras is provided in *AllGroundTruths\_and\_Calibration.zip* - The development and evaluation toolkits are packed in *toolkit.zip* - To process from scratch you can download the raw version of data (*RAWData\_40Hz*) which can be processed with the calibration information from *AllGroundTruths\_and\_Calibration.zip* using the development toolkit in *toolkit.zip*. You are encouraged to use other methods to process the data in case better results can be achieved - To start downloading, switch to the **Data tab** above the dataset title. **Note:** We recommend downloading the compressed files separately using a fixed ethernet connection instead of a wireless internet connection. If you face any issue downloading data or if you have any queries, feel free to contact at <ihtisham.ali@tuni.fi>.
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Year of publication

2020

Authors

Center for Immersive Visual Technologies - Publisher, Creator

Center for Immersive Visual Technologies, Tampere University - Curator

Tampere University

ihtisham ali Orcid -palvelun logo - Creator

Ahmed Durmush - Contributor

Atanas Gotchev - Contributor

Jari Yli-Hietanen - Contributor

Jussi Collin - Contributor

Olli Suominen - Contributor

Sari Peltonen - Contributor

Other information

Fields of science

Electronic, automation and communications engineering, electronics

Language

English

Open access

Open

License

Creative Commons Attribution 4.0 International (CC BY 4.0)

Keywords

3D, autonomous, dataset, forestry, GNSS, GPS, heavy machine, IMU, localization, machine vision, mapping, monocular, Odometry, Sensor Fusion, SLAM, stereo, Visual, computer vision, robots

Subject headings

all-terrain vehicles, autonomous vehicles, forest machine operators, forest roads, heavy equipment, machine safety, machines, maps, navigation
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