3D Traversability Analysis in Forest Environments Based on Mechanical Effort

Carvalho, Afonso E.; Ferreira, João Filipe; Portugal, David

doi:10.1007/978-3-031-22216-0_17

Cited by 4 publications

(4 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As a final, bonus example going beyond our use case scenario, Figure 12 shows the mechanical effort-based traversability technique proposed by Carvalho et al [ 54 ] running with the data provided in our dataset. It uses point clouds to infer terrain gradient and the location of obstacles in space, and from there, it generates a global 2D costmap with mechanical effort information to guide the agent from one place to another in a way that minimizes the mechanical effort it is subject to and potentially its energy/fuel consumption.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

A LiDAR-Camera-Inertial-GNSS Apparatus for 3D Multimodal Dataset Collection in Woodland Scenarios

Cristóvão,

Portugal,

Carvalho

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

Forestry operations have become of great importance for a sustainable environment in the past few decades due to the increasing toll induced by rural abandonment and climate change. Robotics presents a promising solution to this problem; however, gathering the necessary data for developing and testing algorithms can be challenging. This work proposes a portable multi-sensor apparatus to collect relevant data generated by several onboard sensors. The system incorporates Laser Imaging, Detection and Ranging (LiDAR), two stereo depth cameras and a dedicated inertial measurement unit (IMU) to obtain environmental data, which are coupled with an Android app that extracts Global Navigation Satellite System (GNSS) information from a cell phone. Acquired data can then be used for a myriad of perception-based applications, such as localization and mapping, flammable material identification, traversability analysis, path planning and/or semantic segmentation toward (semi-)automated forestry actuation. The modular architecture proposed is built on Robot Operating System (ROS) and Docker to facilitate data collection and the upgradability of the system. We validate the apparatus’ effectiveness in collecting datasets and its flexibility by carrying out a case study for Simultaneous Localization and Mapping (SLAM) in a challenging woodland environment, thus allowing us to compare fundamentally different methods with the multimodal system proposed.

show abstract

Section: Resultsmentioning

confidence: 99%

“… Mechanical effort costmap generated by the method presented in [ 54 ] with data from our dataset, in which lighter values in the grayscale represent easier to traverse areas and vice versa. …”

Section: Figurementioning

confidence: 99%

A LiDAR-Camera-Inertial-GNSS Apparatus for 3D Multimodal Dataset Collection in Woodland Scenarios

Cristóvão,

Portugal,

Carvalho

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…A recent method proposed by Carvalho et al [108] focuses on the concept of mechanical effort to solve the problem of terrain traversability and path planning in 3D forest environments. The technique processes 3D point cloud maps to generate terrain gradient information.…”

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

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…Similarly, in Lee and Chung (2021) , a traversability model was trained using slope, roughness, and curvature features, inferred from eigenvectors and eigenvalues of the covariance matrix of the terrain elevation map. In another example, a large-sized rover in simulation used 3D point cloud data from LiDAR sensors to estimate the gradient of uneven terrain, and consequently quantify the mechanical effort in traversing the terrain Lourenço et al (2020) ; Carvalho et al (2022) . Importantly, while geometry-based approaches for terrain traversability have demonstrated some success in navigating rigid terrains such as on well paved paths in structured urban environments (e.g., Bellone et al, 2017 ; Tang et al, 2019 ; Liu L. et al, 2020 ; Lee and Chung, 2021 ; Lee et al, 2022 ), they may face potential challenges on compliant terrains such as a forest floor, at a low-viewpoint, with an abundance of grass and other soft vegetation where geometry-based features are unreliable ( Haddeler et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

An embarrassingly simple approach for visual navigation of forest environments

et al. 2023

View full text Add to dashboard Cite

Navigation in forest environments is a challenging and open problem in the area of field robotics. Rovers in forest environments are required to infer the traversability of a priori unknown terrains, comprising a number of different types of compliant and rigid obstacles, under varying lighting and weather conditions. The challenges are further compounded for inexpensive small-sized (portable) rovers. While such rovers may be useful for collaboratively monitoring large tracts of forests as a swarm, with low environmental impact, their small-size affords them only a low viewpoint of their proximal terrain. Moreover, their limited view may frequently be partially occluded by compliant obstacles in close proximity such as shrubs and tall grass. Perhaps, consequently, most studies on off-road navigation typically use large-sized rovers equipped with expensive exteroceptive navigation sensors. We design a low-cost navigation system tailored for small-sized forest rovers. For navigation, a light-weight convolution neural network is used to predict depth images from RGB input images from a low-viewpoint monocular camera. Subsequently, a simple coarse-grained navigation algorithm aggregates the predicted depth information to steer our mobile platform towards open traversable areas in the forest while avoiding obstacles. In this study, the steering commands output from our navigation algorithm direct an operator pushing the mobile platform. Our navigation algorithm has been extensively tested in high-fidelity forest simulations and in field trials. Using no more than a 16 × 16 pixel depth prediction image from a 32 × 32 pixel RGB image, our algorithm running on a Raspberry Pi was able to successfully navigate a total of over 750 m of real-world forest terrain comprising shrubs, dense bushes, tall grass, fallen branches, fallen tree trunks, small ditches and mounds, and standing trees, under five different weather conditions and four different times of day. Furthermore, our algorithm exhibits robustness to changes in the mobile platform’s camera pitch angle, motion blur, low lighting at dusk, and high-contrast lighting conditions.

show abstract

3D Traversability Analysis in Forest Environments Based on Mechanical Effort

Cited by 4 publications

References 15 publications

A LiDAR-Camera-Inertial-GNSS Apparatus for 3D Multimodal Dataset Collection in Woodland Scenarios

A LiDAR-Camera-Inertial-GNSS Apparatus for 3D Multimodal Dataset Collection in Woodland Scenarios

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

An embarrassingly simple approach for visual navigation of forest environments

Contact Info

Product

Resources

About