Orchard mapping and mobile robot localisation using on-board camera and laser scanner data fusion – Part A: Tree detection

Shalal, Nagham; Löw, Tobias; McCarthy, Cheryl; Hancock, Nicola

doi:10.1016/j.compag.2015.09.025

Cited by 82 publications

(21 citation statements)

References 12 publications

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“…In [15], the authors report a detection rate of 97.8% and a false-positive rate of 15.2% for an oil-palm tree detection performed with a stereo camera. The algorithm presented in [16] allows to detect persimmon trees thanks to a camera coupled with a laser scanner. Precision rates ranging from 96.77% to 98.94% and recall rates from 93.75% to 96.88% are reported.…”

Section: Methodsmentioning

confidence: 99%

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Tree Detection With Low-Cost Three-Dimensional Sensors for Autonomous Navigation in Orchards

Durand-Petiteville

Flécher

Cadenat

et al. 2018

IEEE Robot. Autom. Lett.

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This paper deals with autonomous farming and with the autonomous navigation of an agricultural robot in orchards. These latter are typical semi-structured environments where the dense canopy prevents from using GPS signal and embedded sensors are often preferred to localize the vehicle. To move safely in such environments, it is necessary to provide the robot the ability of detecting and localizing trees. This paper focuses on this problem. It presents a low cost but efficient vision-based system allowing to detect accurately, quickly and robustly the trees. It is made of four stereo cameras which provide a point cloud characterizing the environment. The key idea is to find the tree trunks by detecting their shadows which are materialized by concavities in the obtained point cloud. In this way, branches and leaves are not taken into account, improving the detection robustness and therefore the navigation strategy. The method has been implemented using ROS and validated using data sequences taken in several different orchards. The obtained results definitely validate the approach and its performances show that the processing time (around 1ms) is sufficiently short for the data to be used at the control level. A comparison with other approaches from the literature is also provided.

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Section: Methodsmentioning

confidence: 99%

“…The work presented in [16] proposes to detect trees using a camera coupled with a laser scanner. It relies on stochastic models, obtained during a pre-navigation step, to detect trees in both Cartesian and image spaces.…”

Section: B Tree Detectionmentioning

confidence: 99%

Tree Detection With Low-Cost Three-Dimensional Sensors for Autonomous Navigation in Orchards

Durand-Petiteville

Flécher

Cadenat

et al. 2018

IEEE Robot. Autom. Lett.

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“…As a result, farmers can avoid excessive use of pesticides, chemical fertilizers, and other resources, and prevent environmental pollution [8]. The use of these orchard agricultural robots can help produce high quality apples, grapes, and other fruits [9,10]. The basic technology for the development of typical orchard farming robots is the recognition of location, that is, localization technology.…”

Section: Introductionmentioning

confidence: 99%

“…When SLAM is applied, the position of trees in a semi-structured orchard environment can be used as an indicator of SLAM and can be used to provide information about localization and search paths for autonomous movement [12]. Shalal et al proposed a multisensory combination method and created SLAM information in a real orchard environment with limited conditions where tree trunks are not covered by branches and leaves [9,10]. Cheein et al provide a histogram of oriented gradients (HOG) where features of tree trunks were extracted to optimize recognition performance and these features were used to train support vector machine (SVM) classifiers to recognize tree trunks [13].…”

Section: Introductionmentioning

confidence: 99%

“…Meanwhile, laser sensors were used to locate moving machines. However, these results show that the tree trunk detection algorithm is constrained when trunks are covered with fallen tree branches and leaves [9,13,16]. In the case of an algorithm that generates autonomous paths in an apple orchard based on a mono camera, the path estimation algorithm cannot guarantee performance when the ground pattern is complicated by weeds and soils [17].…”

Section: Introductionmentioning

confidence: 99%

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Orchard Free Space and Center Line Estimation Using Naive Bayesian Classifier for Unmanned Ground Self-Driving Vehicle

et al. 2018

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Abstract:In the case of autonomous orchard navigation, researchers have developed algorithms that utilize features, such as trunks, canopies, and sky in orchards, but there are still various difficulties in recognizing free space for autonomous navigation in a changing agricultural environment. In this study, we applied the Naive Bayesian classification to detect the boundary between the trunk and the ground and propose an algorithm to determine the center line of free space. The naïve Bayesian classification requires a small number of samples for training and a simple training process. In addition, it was able to effectively classify tree trunk's points and noise points of the orchard, which are problematic in vision-based processing, and noise caused by small branches, soil, weeds, and tree shadows on the ground. The performance of the proposed algorithm was investigated using 229 sample images obtained from an image acquisition system with a Complementary Metal Oxide Semiconductor (CMOS) Image Sensor (CIS) camera. The center line detected by the unaided-eye manual decision and the results extracted by the proposed algorithm were compared and analyzed for several parameters. In all compared parameters, extracted center line was more stable than the manual center line results.

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Preliminary Comparison Between Handheld and Mobile Robotic Mapping Systems

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Beinat

et al. 2021

Proceedings of I4SDG Workshop 2021

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Orchard mapping and mobile robot localisation using on-board camera and laser scanner data fusion – Part A: Tree detection

Cited by 82 publications

References 12 publications

Tree Detection With Low-Cost Three-Dimensional Sensors for Autonomous Navigation in Orchards

Tree Detection With Low-Cost Three-Dimensional Sensors for Autonomous Navigation in Orchards

Orchard Free Space and Center Line Estimation Using Naive Bayesian Classifier for Unmanned Ground Self-Driving Vehicle

Preliminary Comparison Between Handheld and Mobile Robotic Mapping Systems

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