FinnForest dataset: A forest landscape for visual SLAM

Ali, Ihtisham; Durmush, Ahmed; Suominen, Olli; Yli-Hietanen, Jari; Peltonen, Sari; Collin, Jussi; Gotchev, Atanas

doi:10.1016/j.robot.2020.103610

Cited by 22 publications

(17 citation statements)

References 19 publications

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“…The 6-DoF pose regression in the proposed bidirectional case is significantly more challenging compared to traditional cases and is performed with an independent model. We employ two public datasets namely FinnForest Dataset [24] and PennCOSYVIO dataset [25] to conduct our tests for place recognition and pose regression.…”

Section: Related Workmentioning

confidence: 99%

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Bi-directional Loop Closure for Visual SLAM

Ali¹,

Peltonen²,

Gotchev³

2022

Preprint

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Section: Related Workmentioning

confidence: 99%

“…However, most of the datasets do not provide bi-directional motion since they are tailored for handling the problem from a uni-directional perspective. For our work, we found that FinnForest dataset [24] and parts of PennCOSYVIO dataset [25] can be used for training and testing purposes.…”

Section: Data Selection and Dataset Preparationmentioning

confidence: 99%

Bi-directional Loop Closure for Visual SLAM

Ali¹,

Peltonen²,

Gotchev³

2022

Preprint

Self Cite

View full text Add to dashboard Cite

“…In [166], the authors presented a multimodal dataset of laser scans, colour and grey images (available at http://autonomy.cs.sfu.ca/sfu-mountain-dataset, accessed on 24 September 2021), whose data correspond to eight hours of trail navigation. In [167], the authors produced a dataset composed by colour images (available at https://etsin.fairdata.fi/dataset/06926f4b-b36a-4d6e-873c-aa3e7d84ab49, accessed on 24 September 2021) for forestry operations in general. Lastly, in [168], the authors proposed two multimodal datasets made of laser scans and thermal images (available as DS_AG_34 and DS_AG_35 at https://doi.org/10.5281/zenodo.5357238, accessed accessed on 12 October 2021) for forestry robotics, and they used the datasets to perform a SLAM benchmark.…”

Section: Proposed Datasetmentioning

confidence: 99%

“…Eysn et al [34] Laser scans LAS, TIFF, SHP Niu et al [165] Colour and depth images PNG, CSV da Silva et al [31] Visible and thermal images JPG, XML Bruce et al [166] Laser scans; colour and monochrome images ROS Ali et al [167] Colour images ROS Reis et al [168] Laser scans; thermal images ROS QuintaReiFMD Laser scans; visible, thermal, and depth images ROS…”

Section: Reference Perception Data Data Formatmentioning

confidence: 99%

Unimodal and Multimodal Perception for Forest Management: Review and Dataset

et al. 2021

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Robotics navigation and perception for forest management are challenging due to the existence of many obstacles to detect and avoid and the sharp illumination changes. Advanced perception systems are needed because they can enable the development of robotic and machinery solutions to accomplish a smarter, more precise, and sustainable forestry. This article presents a state-of-the-art review about unimodal and multimodal perception in forests, detailing the current developed work about perception using a single type of sensors (unimodal) and by combining data from different kinds of sensors (multimodal). This work also makes a comparison between existing perception datasets in the literature and presents a new multimodal dataset, composed by images and laser scanning data, as a contribution for this research field. Lastly, a critical analysis of the works collected is conducted by identifying strengths and research trends in this domain.

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“…The main limitation of this proposal is the lack of data from different times of the day, or year. In [86] a dataset for test and evaluation of visual SLAM approaches in forests is presented. In this, data are provided from four RGB cameras, an IMU, and a GNSS receiver, all of them calibrated and synchronized.…”

Section: Datasets For Localization and Mapping In Agriculture And Forestrymentioning

confidence: 99%

Localization and Mapping for Robots in Agriculture and Forestry: A Survey

et al. 2020

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Research and development of autonomous mobile robotic solutions that can perform several active agricultural tasks (pruning, harvesting, mowing) have been growing. Robots are now used for a variety of tasks such as planting, harvesting, environmental monitoring, supply of water and nutrients, and others. To do so, robots need to be able to perform online localization and, if desired, mapping. The most used approach for localization in agricultural applications is based in standalone Global Navigation Satellite System-based systems. However, in many agricultural and forest environments, satellite signals are unavailable or inaccurate, which leads to the need of advanced solutions independent from these signals. Approaches like simultaneous localization and mapping and visual odometry are the most promising solutions to increase localization reliability and availability. This work leads to the main conclusion that, few methods can achieve simultaneously the desired goals of scalability, availability, and accuracy, due to the challenges imposed by these harsh environments. In the near future, novel contributions to this field are expected that will help one to achieve the desired goals, with the development of more advanced techniques, based on 3D localization, and semantic and topological mapping. In this context, this work proposes an analysis of the current state-of-the-art of localization and mapping approaches in agriculture and forest environments. Additionally, an overview about the available datasets to develop and test these approaches is performed. Finally, a critical analysis of this research field is done, with the characterization of the literature using a variety of metrics.

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FinnForest dataset: A forest landscape for visual SLAM

Cited by 22 publications

References 19 publications

Bi-directional Loop Closure for Visual SLAM

Bi-directional Loop Closure for Visual SLAM

Unimodal and Multimodal Perception for Forest Management: Review and Dataset

Localization and Mapping for Robots in Agriculture and Forestry: A Survey

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