Canopy density estimation in perennial horticulture crops using 3D spinning lidar SLAM

Löwe, Thomas; Moghadam, Peyman; Edwards, Everard; Williams, Jason L.

doi:10.1002/rob.22006

Cited by 32 publications

(14 citation statements)

References 49 publications

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“…In [101] researchers have developed a canopy density estimation at four separate locations in South Australia using AgScan3D, a mobile vehicle-mounted 3D spinning LiDAR system, to generate a globally registered ray cloud. The AgScan3D is embedded in the rear of a Kubota farm vehicle and consists of a 3D spinning LiDAR, 3DM-Gx3 IMU and a GPS unit.…”

Section: Robotic Applications In Agriculture For Yield Estimation Andmentioning

confidence: 99%

Advances in Agriculture Robotics: A State-of-the-Art Review and Challenges Ahead

2021

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The constant advances in agricultural robotics aim to overcome the challenges imposed by population growth, accelerated urbanization, high competitiveness of high-quality products, environmental preservation and a lack of qualified labor. In this sense, this review paper surveys the main existing applications of agricultural robotic systems for the execution of land preparation before planting, sowing, planting, plant treatment, harvesting, yield estimation and phenotyping. In general, all robots were evaluated according to the following criteria: its locomotion system, what is the final application, if it has sensors, robotic arm and/or computer vision algorithm, what is its development stage and which country and continent they belong. After evaluating all similar characteristics, to expose the research trends, common pitfalls and the characteristics that hinder commercial development, and discover which countries are investing into Research and Development (R&D) in these technologies for the future, four major areas that need future research work for enhancing the state of the art in smart agriculture were highlighted: locomotion systems, sensors, computer vision algorithms and communication technologies. The results of this research suggest that the investment in agricultural robotic systems allows to achieve short—harvest monitoring—and long-term objectives—yield estimation.

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Section: Robotic Applications In Agriculture For Yield Estimation Andmentioning

confidence: 99%

Advances in Agriculture Robotics: A State-of-the-Art Review and Challenges Ahead

2021

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“…Similar work by Nellithimaru and Kantor (2019) presented a method to count fruits and estimate yield and used SLAM combined with Deep Learning techniques to accurately reconstruct the features of grapes fitting with spheres. T. Lowe et al (2021) proposed a novel, canopy density estimation solution using a 3D ray cloud representation for perennial horticultural crops at the field scale. In the proposed method, the AgScan3D (a spinning Lidar payload) data are processed through a Continuous‐Time SLAM algorithm into a globally registered 3D ray cloud.…”

Section: Applications In Agriculturementioning

confidence: 99%

Recent developments and applications of simultaneous localization and mapping in agriculture

Ding

Zhang

Zhou

et al. 2022

Journal of Field Robotics

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Simultaneous Localization and Mapping (SLAM) is a process to use multiple sensors to position an unmanned mobile vehicle without previous knowledge of the environment, and meanwhile construct a map of this environment for the further applications. Over the past three decades, SLAM has been intensively researched and widely applied in mobile robot control and unmanned vehicle navigation. SLAM technology has demonstrated a great potential in autonomously navigating the mobile robot and simultaneously reconstructing the three-dimensional (3D) information of surrounding environment. With the vigorous driving of sensor technology and 3D reconstruction algorithms, many attempts have been conducted to propose novel systems and algorithms combined with different sensors to solve the SLAM problem. Notably, SLAM has been extended to various aspects of agriculture involved with autonomous navigation, 3D mapping, field monitoring, and intelligent spraying. This paper focuses on the recent developments and applications of SLAM, particularly in complex and unstructured agricultural environment.A detailed summary of the developments of SLAM is given from three main fundamental types: light detection and ranging SLAM, Visual SLAM, and Sensor Fusion SLAM, and we also discuss the applications and prospects of SLAM technology in agricultural mapping, agricultural navigation, and precise automatic agriculture. Particular attention has been paid to the SLAM sensors, systems, and algorithms applied in agricultural tasks. Additionally, the challenges and future trends of SLAM are reported.

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“…Authors in [21], found GMapping to be the most reliable among KartoSLAM [22] and Google-Cartographer [23] although, these algorithms are considered to be efficient only on planar fields. In more recent work described in [9], a SLAM method based on a 3D Lidar data fused with GPS measurements, created 3D maps for the canopy density estimation. The method exhibited accurate results for long travelled distance.…”

Section: A Mapping and Localization In Vineyardsmentioning

confidence: 99%

“…Some approaches focus on the use of cameras in GPS-denied environments [6], [7], [8] to incrementally create a map relied on vineyards structured features. High resolution 3D Lidar sensors are also used to provide dense vineyards reconstruction [9], yet the high-cost of such sensors still hinders the adoption of such solutions. Semantic SLAM methods have also been presented, tailored to vineyards mapping needs, but tested on limited ranges and data [10], mainly due to excessive demands on computational resources.…”

Section: Introductionmentioning

confidence: 99%

Loop Closure Detection and SLAM in Vineyards with Deep Semantic Cues

Papadimitriou

Kleitsiotis

Kostavelis

et al. 2022

2022 International Conference on Robotics and Automation (ICRA)

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Automation of vineyards cultivation necessitates for mobile robots to retain accurate localization system. The paper introduces a stereo vision-based Graph-Simultaneous Localization and Mapping (Graph-SLAM) pipeline customtailored to the specificities of vineyard fields. Graph-SLAM is reinforced with a Loop Closure Detection (LCD) based on semantic segmentation of the vine trees. The Mask R-CNN network is applied to segment the trunk regions of images, on which unique visual features are extracted. These features are used to populate the bag of visual words (BoVWs) retained on the formulated graph. A nearest neighbor search is applied to each query trunk-image to associate each unique feature descriptor with the corresponding node in the graph using a voting procedure. We apply a probabilistic method to select the most suitable loop closing pair and, upon an LCD appearance, the 3D points of the trunks are employed to estimate the loop closure constraint to the graph. The traceable features on trunk segments drastically reduce the number of retained BoVWs, which in turn expedites significantly the loop closure and graph optimization, rendering our method suitable for large scale mapping in vineyards. The pipeline has been evaluated on several data sequences gathered from real vineyards, in different seasons, when the appearance of vine trees vary significantly, and exhibited robust mapping in long distances.

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Canopy density estimation in perennial horticulture crops using 3D spinning lidar SLAM

Cited by 32 publications

References 49 publications

Advances in Agriculture Robotics: A State-of-the-Art Review and Challenges Ahead

Advances in Agriculture Robotics: A State-of-the-Art Review and Challenges Ahead

Recent developments and applications of simultaneous localization and mapping in agriculture

Loop Closure Detection and SLAM in Vineyards with Deep Semantic Cues

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