Row following in pergola structured orchards

Bell, Jamie; MacDonald, Bruce A.; Ahn, Ho Seok

doi:10.1109/iros.2016.7759120

Cited by 18 publications

(11 citation statements)

References 9 publications

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“…Although traditionally used in indoor applications (e.g., industry and warehouses), the past decade reducing costs sparked interest in using such technology in new areas such as agriculture. In the case of row‐following tasks, most research with LiDAR is focused on orchards (Barawid, Mizushima, Ishii, & Noguchi, ; Bell, MacDonald & Ahn, ; Bergerman et al, ; Zhang, Chambers, Maeta, Bergerman, & Singh, ). For well‐structured tree rows, 2D LiDAR suffices to provide enough information for row following (Bergerman et al, ).…”

Section: Related Workmentioning

confidence: 99%

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Under canopy light detection and ranging‐based autonomous navigation

Higuti

Velasquez

Magalhães

et al. 2018

Journal of Field Robotics

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This paper describes a light detection and ranging (LiDAR)-based autonomous navigation system for an ultralightweight ground robot in agricultural fields. The system is designed for reliable navigation under cluttered canopies using only a 2D Hokuyo UTM-30LX LiDAR sensor as the single source for perception. Its purpose is to ensure that the robot can navigate through rows of crops without damaging the plants in narrow row-based and high-leaf-cover semistructured crop plantations, such as corn (Zea mays) and sorghum (Sorghum bicolor). The key contribution of our work is a LiDAR-based navigation algorithm capable of rejecting outlying measurements in the point cloud due to plants in adjacent rows, low-hanging leaf cover or weeds. The algorithm addresses this challenge using a set of heuristics that are designed to filter out outlying measurements in a computationally efficient manner, and linear least squares are applied to estimate within-row distance using the filtered data.Moreover, a crucial step is the estimate validation, which is achieved through a heuristic that grades and validates the fitted row-lines based on current and previous information. The proposed LiDAR-based perception subsystem has been extensively tested in production/breeding corn and sorghum fields. In such variety of highly cluttered real field environments, the robot logged more than 6 km of autonomous run in straight rows. These results demonstrate highly promising advances to LiDARbased navigation in realistic field environments for small under-canopy robots.

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Section: Related Workmentioning

confidence: 99%

“…But when ground vegetation occludes tree trunks, Zhang et al () use a rotating 2D LiDAR to retrieve a 3D point cloud and then accurately obtain robot’s position. Similarly, Bell et al () employs a Velodyne VLP16 (3D LiDAR) to handle sparseness in pergola structured orchards.…”

Section: Related Workmentioning

confidence: 99%

Under canopy light detection and ranging‐based autonomous navigation

Higuti

Velasquez

Magalhães

et al. 2018

Journal of Field Robotics

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“…In addition, Weiss and Biber [22] stated that in contrast to most stereo vision cameras, the ranging information is calculated inside the LiDAR sensor; thus, no further time-consuming calculation needs to be carried out on the computer, while the precision of distance values is also independent of the measured distances. Bell et al [3] implemented a row following algorithm using a 2D LiDAR for pergola structured orchards, where the trees are more spaced and not forming two aligned rows.…”

Section: Introductionmentioning

confidence: 99%

Improved localization in a corn crop row using a rotated laser rangefinder for three-dimensional data acquisition

Gasparino

Higuti

Velasquez

et al. 2020

J Braz. Soc. Mech. Sci. Eng.

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Small robotic vehicles have been navigating agricultural fields in the pursuit of new possibilities to increase agricultural production and to meet the increasing food and energetic demands. However, a perception system with reliable awareness of the surroundings remains a challenge to achieve autonomous navigation. Camera and single-layer laser scanners have been the primary sources of information, yet the first suffers from outdoor light sensibility and both from occlusion by leaves. This paper describes a three-dimensional system acquisition for corn crops. The sensing core is a single-layer UTM30-LX laser scanner rotating around its axis, while an inertial sensor provides angular measurements. With the rotation, multiple layers are used to compose a 3D point cloud, which is represented by a two-dimensional occupancy grid. Each cell is filled according to the number of readings, and their weights derive from two procedures: firstly, a mask enhances vertical entities (stalks); secondly, two Gaussian functions on the expected position of the immediate neighboring rows weaken readings in the middle of the lane and farther rows. The resulting occupancy grid allows the representation of the cornrows by virtual walls, which are used as references to a wall follower algorithm. According to experimental results, the virtual walls are segmented with reduced influence from straying leaves and sparse weeds when compared to the segmentation done with single-layer laser scanner data. Indeed, 64.02% of 3D outputs are within 0.05 m limit error from expected lane width, while only 11.63% of single-layer laser data are within same limit.

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“…In the case of row following tasks, most LiDAR researches aimed orchards (BARAWID, 2007;Ji Zhang, 2013;BERGERMAN, 2015;BELL, 2016;LEMOS, 2018). Figure 9 shows two examples of such works.…”

Section: Light Detection and Ranging (Lidar)mentioning

confidence: 99%

“…A commonly employed LiDAR solution when ground vegetation occludes tree trunks is the use of 3-D point clouds (BELL, 2016;Ji Zhang, 2013). The first uses a Velodyne VLP-16 3D laser scanner to enable autonomous navigation of a Pioneer P3-AT robot in pergola structured orchards (Fig.…”

Section: Light Detection and Ranging (Lidar)mentioning

confidence: 99%

2D LiDAR-based perception for under canopy autonomous scouting of small ground robots within narrow lanes of agricultural fields

Higuti¹

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Lightweight ground robots have been increasingly showing their potential to tackle agricultural tasks that require time-consuming human labor thus limited in detail or area coverage. A task that benefits several modern agricultural practices is scouting (walking though the field). However, a reliable autonomous navigation is still a challenge. A robot needs to deal with the agricultural field dynamism and unpredictable obstacles, e.g. humans and machines. In particular, corn and sorghum present an additional issue due to the standard way of cultivation: Narrow sub-meter lanes that become even less visible on later stages due to dense coverage and spreading of leaves. This condition heavily influences the sensors by provoking frequent occlusions, misreadings and other situations out of their working range. In such context, three questions arise: 1) Can the unexplored potential of Light Detection and Ranging (LiDAR) sensor suffice to interpret a narrow lane crop environment without artificial landmarks? 2) Does the search of a best line representation for crop rows really represent the problem? 3) How can lateral distance estimation be improved through sensor fusion? To answer these three questions, Perception LiDAR (PL) has been developed to estimate lateral distance to crop rows using a 2D LiDAR as the core sensor. An Extended Kalman Filter enables the use of embedded odometry and inertial measurements. Manual ground truth has shown that the task of finding best line representation from LiDAR data is challenging, and it could have as low as 54% of line estimates within 0.05 m error. Nonetheless, the proposed method has enabled 72 km of autonomous operation of multiple robots. Due to unreliable RTK-GNSS signal under canopy, PL outputs significantly outperform the GNSS-based positioning, which may not even be in the current lane. The extensive field testing happened in multiple corn and sorghum fields between 2017 and 2020. For continuous lane, i.e. at least one of the side rows exists, the success rate to finish the desired segment of autonomous navigation is 89.76%. The higher performance for LiDAR input with significant less presence of objects other than stalks, and also the failure concentration on sensor occlusion and gaps, both situations with low to none visible rows, strongly indicates that the problem is not best line fitting, but rather a classification one where the end goal is to find stalks. In summary, although PL does not provide a fully intervention-free for within crop rows navigation, its current capabilities relieve operators from the tedious task of manually driving the robot the whole time and pave the way towards a fully autonomous agricultural robot.

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Row following in pergola structured orchards

Cited by 18 publications

References 9 publications

Under canopy light detection and ranging‐based autonomous navigation

Under canopy light detection and ranging‐based autonomous navigation

Improved localization in a corn crop row using a rotated laser rangefinder for three-dimensional data acquisition

2D LiDAR-based perception for under canopy autonomous scouting of small ground robots within narrow lanes of agricultural fields

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