FinnForest : Metsämaisema visual SLAM

Kuvaus

## 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>.
Näytä enemmän

Julkaisuvuosi

2020

Aineiston tyyppi

Tekijät

Center for Immersive Visual Technologies - Julkaisija, Tekijä

Center for Immersive Visual Technologies, Tampere University - Kuraattori

Tampere University

Ahmed Durmush - Muu tekijä

Atanas Gotchev - Muu tekijä

Jari Yli-Hietanen - Muu tekijä

Jussi Collin - Muu tekijä

Olli Suominen - Muu tekijä

Sari Peltonen - Muu tekijä

ihtisham ali Orcid -palvelun logo - Tekijä

Projekti

Muut tiedot

Tieteenalat

Sähkö-, automaatio- ja tietoliikennetekniikka, elektroniikka

Kieli

englanti

Saatavuus

Avoin

Lisenssi

Creative Commons Nimeä 4.0 Kansainvälinen (CC BY 4.0)

Avainsanat

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

Asiasanat

robotit, kalusto (elottomat fyysiset kokonaisuudet), kartat, koneet, konenäkö, koneturvallisuus, metsäkoneenkuljettajat, metsätiet, mönkijät, navigointi, robottiautot

Ajallinen kattavuus

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