SLAM in the Field: An Evaluation of Monocular Mapping and Localization on Challenging Dynamic Agricultural Environment

Shu, Fangwen; Lesur, Paul; Xie, Yaxu; Pagani, Alain; Stricker, Didier

doi:10.1109/wacv48630.2021.00180

Cited by 34 publications

(19 citation statements)

References 52 publications

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“…A similar platform was proposed by Dong et al (2017) to realize 4D field reconstruction for crop monitoring. Shu et al (2021) introduced a system capable of combining a sparse, indirect, monocular Visual SLAM, with both offline and real‐time MultiView Stereo reconstruction algorithms. The combination of the above system overcomes many obstacles encountered by autonomous vehicles or robots employed in agricultural environments, such as overly repetitive patterns, need for very detailed reconstructions, and abrupt movements caused by uneven roads.…”

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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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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Section: Simultaneous Localization and Mapping (Slam) In Agricultural...mentioning

confidence: 99%

“…Researchers from the French-German MOVEON project have recently demonstrated a comprehensive breakdown of a visual SLAM system based only on a single camera combined with state-of-the-art multi-view stereo (MVS) algorithms and DL techniques [27]. It SLAM is a proper solution for occluded GPS (sometimes blocked by dense foliage), crop-relative guidance in open fields, tree-relative guidance in orchards, and more importantly, sensing the crops and its environment [27]. Various sensors, such as on-board cameras, laser scanners and ultrasonic devices, have been used to extract features from the crops themselves and to determine the location of the robot related to the crop lines or tree rows to auto-steer.…”

Section: Simultaneous Localization and Mapping (Slam) In Agricultural...mentioning

confidence: 99%

Section: Simultaneous Localization and Mapping (Slam) In Agricultural...mentioning

confidence: 99%

See 1 more Smart Citation

Data-Driven Artificial Intelligence Applications for Sustainable Precision Agriculture

Linaza

Posada

Bund³

et al. 2021

Agronomy

Self Cite

125

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One of the main challenges for the implementation of artificial intelligence (AI) in agriculture includes the low replicability and the corresponding difficulty in systematic data gathering, as no two fields are exactly alike. Therefore, the comparison of several pilot experiments in different fields, weather conditions and farming techniques enhances the collective knowledge. Thus, this work provides a summary of the most recent research activities in the form of research projects implemented and validated by the authors in several European countries, with the objective of presenting the already achieved results, the current investigations and the still open technical challenges. As an overall conclusion, it can be mentioned that even though in their primary stages in some cases, AI technologies improve decision support at farm level, monitoring conditions and optimizing production to allow farmers to apply the optimal number of inputs for each crop, thereby boosting yields and reducing water use and greenhouse gas emissions. Future extensions of this work will include new concepts based on autonomous and intelligent robots for plant and soil sample retrieval, and effective livestock management.

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“…Due to the increasing computational power of computers [33], VSLAM localization principles became more popular for indoor localization purposes. VSLAM principles were only used a few times in agricultural applications [29,34]. Therefore, it can be seen that the usage of VSLAM algorithms for UAV localization tasks in indoor agricultural environments is still at an early stage.…”

Section: Introductionmentioning

confidence: 99%

Visual SLAM for Indoor Livestock and Farming Using a Small Drone with a Monocular Camera: A Feasibility Study

Krul

Pantos

Frangulea

et al. 2021

Drones

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Real-time data collection and decision making with drones will play an important role in precision livestock and farming. Drones are already being used in precision agriculture. Nevertheless, this is not the case for indoor livestock and farming environments due to several challenges and constraints. These indoor environments are limited in physical space and there is the localization problem, due to GPS unavailability. Therefore, this work aims to give a step toward the usage of drones for indoor farming and livestock management. To investigate on the drone positioning in these workspaces, two visual simultaneous localization and mapping (VSLAM)—LSD-SLAM and ORB-SLAM—algorithms were compared using a monocular camera onboard a small drone. Several experiments were carried out in a greenhouse and a dairy farm barn with the absolute trajectory and the relative pose error being analyzed. It was found that the approach that suits best these workspaces is ORB-SLAM. This algorithm was tested by performing waypoint navigation and generating maps from the clustered areas. It was shown that aerial VSLAM could be achieved within these workspaces and that plant and cattle monitoring could benefit from using affordable and off-the-shelf drone technology.

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SLAM in the Field: An Evaluation of Monocular Mapping and Localization on Challenging Dynamic Agricultural Environment

Cited by 34 publications

References 52 publications

Recent developments and applications of simultaneous localization and mapping in agriculture

Recent developments and applications of simultaneous localization and mapping in agriculture

Data-Driven Artificial Intelligence Applications for Sustainable Precision Agriculture

Visual SLAM for Indoor Livestock and Farming Using a Small Drone with a Monocular Camera: A Feasibility Study

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