Graph-based methods for analyzing orchard tree structure using noisy point cloud data

Westling, Fredrik; Underwood, James; Bryson, Mitch

doi:10.1016/j.compag.2021.106270

Cited by 19 publications

(5 citation statements)

References 35 publications

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“…This group has contributed to an important review that focused on harvesting robots [61] and has recently worked on sweet pepper maturity classification [97] and grapevine detection using thermal imaging [98], paving the way for future efforts in automated harvesting. Another important group is the forestry research group from the Australian Centre for Field Robotics at the University of Sydney; this group has been working on tree detection [99,100] and the use of LiDAR and UAV photogrammetry in forestry resources [101].…”

Section: Research and Development Throughout The Rest Of The Worldmentioning

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: Research and Development Throughout The Rest Of The Worldmentioning

confidence: 99%

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

Ferreira,

Portugal,

Andrada

et al. 2023

Robotics

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“…In recent years, there have been a large number of studies on ground point cloud extraction and segmentation algorithms. For example, some scholars segmented ground point clouds based on the ground measurement model of LIDAR [47,48] or used RANSAC to fit the ground like a piece of a plane for segmentation [49][50][51], or distinguished ground and vegetation based on the threshold between ground point clouds [52]. These methods are simple and effective, but considering that the ground point cloud changes continuously above and below the horizontal plane when the robot is driving continuously in the orchard, the principle of a simulation filter (CSF) algorithm [53] is more indicative of the current terrain features than methods that rely on the height of a laser beam, a sensor or set thresholds.…”

Section: Master Robot Navigation Phasementioning

confidence: 99%

Development of a Combined Orchard Harvesting Robot Navigation System

et al. 2022

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Our research concerned the development of an autonomous robotic navigation system for orchard harvesting with a dual master-slave mode, the autonomous navigation tractor orchard transport robot being the master followed by a navigation orchard picking robot as the slave. This addresses the problem that in single master-slave navigation mode, agricultural combined harvesting equipment cannot stop repeatedly between rows of apple trees and drive continuously when turning. According to distances obtained from a global positioning system (GNSS), ground points were used to switch the navigation mode of the transport and picking robot. A cloth simulation filter (CSF) and random sample consensus (RANSAC) algorithm was used to obtain inter-row waypoints. The GNSS point was manually selected as the turn waypoint of the master and a kinematic model was used to compute the turn waypoints of the slave. Finally, we used a pure pursuit algorithm to track these waypoints sequentially to achieve master-slave navigation and ground head master-slave command navigation. The experimental results show that the data packet loss rate was less than 1.2% when the robot communicated in the orchard row within 50 m which meets the robot orchard communication requirements. The master-slave robot can achieve repeated stops in the row using follow navigation, which meets the demands of joint orchard harvesting. The maximum, minimum, mean and standard deviation of position deviation of the master robot were 5.3 cm, 0.8 cm, 2.4 cm, and 0.9 cm, respectively. The position deviations of the slave robot were larger than those of the master robot, with maximum, minimum, mean and standard deviation of 39.7 cm, 1.1 cm, 4.1 cm, and 5.6 cm, respectively. The maximum, minimum, mean and standard deviation of the following error between the master-slave robot were 4.4 cm, 0 cm, 1.3 cm, and 1 cm respectively. Concerning the ground head turn, the command navigation method allowed continuous turning, but the lateral deviation between robots was more than 0.3 m and less than 1 m, and the heading deviation was more than 10° and less than 90°.

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“…When measuring the trunk, the cross section of the trunk was regarded as a standard circle in this research, and the camera scanning of the trunk is shown in Figure 13. The diameter of the trunk could be obtained from the following equation, and the trunk diameter from in Equation (10).…”

Section: 𝑦 = 𝑦 − 𝑦 𝑥 − 𝑥 𝑥 − (𝑦 − 𝑦 )𝑥 𝑥 − 𝑥 + 𝑦mentioning

confidence: 99%

“…Tree trunk diameter measurement is an important step in the harvesting process of the vibration harvesting robot and the result of trunk segmentation directly affects the accuracy of trunk diameter measurement. Trunk segmentation often uses RGB images to extract the trunk, but, thanks to the development of hardware such as laser scanners and laser radars, many scholars have used the point cloud method to reconstruct and segment trunks [9][10][11][12]. However, this method can only extract one piece of trunk information at a time, and its detection range is small.…”

Section: Introductionmentioning

confidence: 99%

A Diameter Measurement Method of Red Jujubes Trunk Based on Improved PSPNet

Qiao

Zheng

et al. 2022

Agriculture

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A trunk segmentation and a diameter measurement of red jujubes are important steps in harvesting red jujubes using vibration harvesting robots as the results directly affect the effectiveness of the harvesting. A trunk segmentation algorithm of red jujubes, based on improved Pyramid Scene Parsing Network (PSPNet), and a diameter measurement algorithm to realize the segmentation and diameter measurement of the trunk are proposed in this research. To this end, MobilenetV2 was selected as the backbone of PSPNet so that it could be adapted to embedded mobile applications. Meanwhile, the Convolutional Block Attention Module (CBAM) was embedded in the MobilenetV2 to enhance the feature extraction capability of the model. Furthermore, the Refinement Residual Blocks (RRBs) were introduced into the main branch and side branch of PSPNet to enhance the segmentation result. An algorithm to measure trunk diameter was proposed, which used the segmentation results to determine the trunk outline and the normal of the centerline. The Euclidean distance of the intersection point of the normal with the trunk profile was obtained and its average value was regarded as the final trunk diameter. Compared with the original PSPNet, the Intersection-over-Union (IoU) value, PA value and Fps of the improved model increased by 0.67%, 1.95% and 1.13, respectively, and the number of parameters was 5.00% of that of the original model. Compared with other segmentation networks, the improved model had fewer parameters and better segmentation results. Compared with the original network, the trunk diameter measurement algorithm proposed in this research reduced the average absolute error and the average relative error by 3.75 mm and 9.92%, respectively, and improved the average measurement accuracy by 9.92%. To sum up, the improved PSPNet jujube trunk segmentation algorithm and trunk diameter measurement algorithm can accurately segment and measure the diameter in the natural environment, which provides a theoretical basis and technical support for the clamping of jujube harvesting robots.

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Graph-based methods for analyzing orchard tree structure using noisy point cloud data

Cited by 19 publications

References 35 publications

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

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

Development of a Combined Orchard Harvesting Robot Navigation System

A Diameter Measurement Method of Red Jujubes Trunk Based on Improved PSPNet

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