Data augmentation using image-to-image translation for detecting forest strip roads based on deep learning

Usui, Kengo

doi:10.1080/14942119.2021.1831426

Cited by 5 publications

(2 citation statements)

References 29 publications

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“…As expected, a clear prevalence of deep learning-based methods with different sensor modalities to tackle terrain classification and image segmentation problems has recently been observed. For instance, in [222], a deep neural network takes an image as input and categorizes every pixel of the image into an assigned class. After a coordinate frame transformation, this can assist the robot's navigation system in traversing the environment.…”

Section: Traversability Analysis For Navigationmentioning

confidence: 99%

Sensing and Artificial Perception for Robots in Precision Forestry: A Survey

Ferreira,

Portugal,

Andrada

et al. 2023

Robotics

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Artificial perception for robots operating in outdoor natural environments, including forest scenarios, has been the object of a substantial amount of research for decades. Regardless, this has proven to be one of the most difficult research areas in robotics and has yet to be robustly solved. This happens namely due to difficulties in dealing with environmental conditions (trees and relief, weather conditions, dust, smoke, etc.), the visual homogeneity of natural landscapes as opposed to the diversity of natural obstacles to be avoided, and the effect of vibrations or external forces such as wind, among other technical challenges. Consequently, we propose a new survey, describing the current state of the art in artificial perception and sensing for robots in precision forestry. Our goal is to provide a detailed literature review of the past few decades of active research in this field. With this review, we attempted to provide valuable insights into the current scientific outlook and identify necessary advancements in the area. We have found that the introduction of robotics in precision forestry imposes very significant scientific and technological problems in artificial sensing and perception, making this a particularly challenging field with an impact on economics, society, technology, and standards. Based on this analysis, we put forward a roadmap to address the outstanding challenges in its respective scientific and technological landscape, namely the lack of training data for perception models, open software frameworks, robust solutions for multi-robot teams, end-user involvement, use case scenarios, computational resource planning, management solutions to satisfy real-time operation constraints, and systematic field testing. We argue that following this roadmap will allow for robotics in precision forestry to fulfil its considerable potential.

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Section: Traversability Analysis For Navigationmentioning

confidence: 99%

Sensing and Artificial Perception for Robots in Precision Forestry: A Survey

Ferreira,

Portugal,

Andrada

et al. 2023

Robotics

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“…Typically, traversability estimation is applied on these discrete representations by assigning costs to distinct portions of terrain. These include probabilistic [10], semantic segmentation [11] and neural network (NN) depth maps [12] approaches. Finally, classical planning algorithms, e.g.…”

Section: Introductionmentioning

confidence: 99%

3D Traversability Analysis in Forest Environments Based on Mechanical Effort

Carvalho

Ferreira

Portugal

2023

Intelligent Autonomous Systems 17

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Autonomous navigation in harsh and dynamic 3D environments poses a great challenge for modern Robotics. This work presents a novel traversability analysis and path-planning technique that processes 3D pointcloud maps to generate terrain gradient information. An analysis of terrain roughness and presence of obstacles is applied on the perceived environment in order to generate efficient paths. These avoid major hills when more conservative paths are available, potentially promoting fuel economy and reducing the wear of the equipment and the associated risks. The proposed approach outperforms existing techniques based on results in realistic 3D simulation scenarios, which are discussed in detail.

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