Orchard mapping and mobile robot localisation using on-board camera and laser scanner data fusion – Part B: Mapping and localisation

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

doi:10.1016/j.compag.2015.09.026

Cited by 70 publications

(36 citation statements)

References 17 publications

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“…Galip et al [46] introduced a novel approach to obtain trajectories of targets from laser scanned datasets. Shalal et al [47] proposed an orchard mapping and mobile robot localisation using onboard camera and laser scanner data. Leigh et al [48] hinted an algorithm for person tracking and following with 2D laser scanners.…”

Section: State-of-the-art Methodsmentioning

confidence: 99%

Untitled

2019

IJCSE

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Laser scanners have a lot of advantages over conventional video cameras. Data processing in laser scanner system becomes faster and easier because there is no need to record real world videos. Besides, the problem of private life conservation is taken away. This paper proposes an approach to track objects from laser scanned dataset. Data points collected by each scan of laser scanners are considered as video frame. Support vector machine (SVM) along with Hungarian algorithm and Kalman filter are used to obtain trajectories of objects from the laser scanned dataset. Experimental results on the same laser scanned datasetshow that the method of SVM with Hungarian algorithm and Kalman filter performs better than that of its alternative having various thresholds with Hungarian algorithm and Kalman filter.

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Section: State-of-the-art Methodsmentioning

confidence: 99%

Untitled

2019

IJCSE

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“…In that sense, sensors such as LiDAR and RGB cameras along with new algorithms for either 2D or 3D dynamic navigation indeed provide increased flexibility in such a unpredictable environment [ 21 , 31 , 32 ]. For instance, in [ 33 , 34 ], the authors developed an algorithm for detecting tree trunks using data fusion from camera and laser scanner. The system developed could navigate throughout a fruit orchard .…”

Section: Introductionmentioning

confidence: 99%

Vinobot and Vinoculer: Two Robotic Platforms for High-Throughput Field Phenotyping

Shafiekhani

Kadam

Fritschi

et al. 2017

Sensors

114

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In this paper, a new robotic architecture for plant phenotyping is being introduced. The architecture consists of two robotic platforms: an autonomous ground vehicle (Vinobot) and a mobile observation tower (Vinoculer). The ground vehicle collects data from individual plants, while the observation tower oversees an entire field, identifying specific plants for further inspection by the Vinobot. The advantage of this architecture is threefold: first, it allows the system to inspect large areas of a field at any time, during the day and night, while identifying specific regions affected by biotic and/or abiotic stresses; second, it provides high-throughput plant phenotyping in the field by either comprehensive or selective acquisition of accurate and detailed data from groups or individual plants; and third, it eliminates the need for expensive and cumbersome aerial vehicles or similarly expensive and confined field platforms. As the preliminary results from our algorithms for data collection and 3D image processing, as well as the data analysis and comparison with phenotype data collected by hand demonstrate, the proposed architecture is cost effective, reliable, versatile, and extendable.

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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%

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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Orchard mapping and mobile robot localisation using on-board camera and laser scanner data fusion – Part B: Mapping and localisation

Cited by 70 publications

References 17 publications

Untitled

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Vinobot and Vinoculer: Two Robotic Platforms for High-Throughput Field Phenotyping

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

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