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Which and how many soil sensors are ideal to predict key soil properties: A case study with seven sensors

GEODERMA 2024年 450卷

作者： Schmidinger, J. Barkov, V. Tavakoli, H. Correa, J. Ostermann, M. Atzmueller, M. Gebbers, R. Vogel, S. Osnabruck Univ Joint Lab Artificial Intelligence & Data Sci Osnabruck Germany Leibniz Inst Agr Engn & Bioecon ATB Dept Agromechatron Potsdam Germany BAM Fed Inst Mat Res & Testing Proc Analyt Technol Berlin Germany German Res Ctr Artificial Intelligence DFKI Res Dept Plan Based Robot Control Osnabruck Germany Osnabruck Univ Semant Informat Syst Grp Osnabruck Germany Martin Luther Univ Halle Wittenberg Inst Agr & Nutr Sci Halle Germany

Soil sensing enables rapid and cost-effective soil analysis. However, a single sensor often does not generate enough information to reliably predict a wide range of soil properties. Within a case-study, our objective was to identify how many and which combinations of soil sensors prove to be suitable for high-resolution soil mapping. On a subplot of an agricultural field showing a high spatial soil variability, six in-situ proximal soil sensors (PSSs) next to remote sensing (RS) data from Sentinel-2 were evaluated based on their capabilities to predict a set of soil properties including: soil organic carbon, pH, moisture as well as plant-available phosphorus, magnesium and potassium. The set of PSSs consisted of ion-selective pH electrodes, a capacitive soil moisture sensor, an apparent soil electrical conductivity measuring system as well as passive gamma-ray-, X-ray fluorescence- and near- infrared spectroscopy. All possible combinations of sensors were exhaustively evaluated and ranked based on their prediction performances using model stacking. Over all soil properties, data fusion demonstrated a considerable increase in prediction accuracy. Five out of six soil properties were predicted with an R-2 >= 0.80 with the best sensor fusion model. Nonetheless, the improvement derived from fusing an increasing number of PSSs was subject to diminishing returns. Sometimes adding more PSSs even decreased prediction performances. Gamma-ray spectroscopy and near-infrared spectroscopy demonstrated to be most effective, both as single sensors or in combination with other sensors. As a single sensor, RS outperformed three out of six PSSs. RS showed especially potential for fusion with single PSSs but was of limited benefit when multiple PSSs were fused. Model stacking proved to be more robust than using single base-models because sensor performances were less model-dependent.

关键词： Proximal soil sensing Remote sensing Sensor fusion Machine learning Precision agriculture In-situ measurements

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AI-based Maize and Weeds Detection on the Edge with CornWeed Dataset

AI-Based Maize and Weeds Detection on the Edge with CornWeed...

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Federated Conference on Computer Science and Information Systems (FedCSIS)

作者： Naeem Iqbal Christoph Manss Christian Scholz Daniel König Matthias Igelbrink Arno Ruckelshausen Plan-based Robot Control DFKI Osnabrueck Germany Marine Perception DFKI Oldenburg Germany Faculty of Engineering and Computer Science University of Applied Sciences Osnabrueck Osnabrueck Germany

Artificial intelligence (AI) is used more heavily in agricultural applications. Yet, the lack of wireless-fidelity (Wi-Fi) connections on agricultural fields makes AI cloud services unavailable. Consequently, AI models have to be processed directly on the edge. In this paper, we evaluate state-of-the-art detection algorithms for their use in agriculture, in particular plant detection. Thus, this paper presents the CornWeed data set, which has been recorded on farm machines, showing labelled maize crops and weeds for plant detection. The paper provides accuracies for the state-of-the-art detection algorithms on the CornWeed data set, as well as frames per second (FPS) metrics for the considered networks on multiple edge devices. Moreover, for the FPS analysis, the detection algorithms are converted to open neural network exchange (ONNX) and TensoRT engine files as they could be used as future standards for model exchange.

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Monocular Localization in Feature-Annotated 3D Polygon Maps

Monocular Localization in Feature-Annotated 3D Polygon Maps

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European Conference on Mobile robots (ECMR)

作者： Alexander Mock Thomas Wiemann Joachim Hertzberg Knowledge Based Systems and Autonomous Robotics Groups at the Institute of Computer Science Osnabrück University Osnabrück Germany German Research Center for Artificial Intelligence (DFKI) Niedersachsen Lab Plan-Based Robot Control Group Osnabrück Germany

Localization in six degrees of freedom is becoming increasingly relevant, especially in indoor environments where GPS is not available. To localize autonomous vehicles like UAVs in such areas, reliable methods for self-localization with low-weight sensors are required. In this paper, we present an approach to precisely localize systems with monocular cameras in polygonal 3D maps annotated with keypoints and feature descriptors computed from LiDAR data and associated reference images. Our contribution consists of offline map computation from high resolution 3D point clouds with corresponding reference images as well as online localization within these maps using low cost sensors. During localization, features extracted from the vehicle’s camera image stream are matched against the reference map. The proposed method is capable of real-time localization and suitable for precise global localization. The evaluation shows comparable results to state of the art with high re-localization accuracy.

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Segmentation of Multibeam Echosounder Bathymetry and Backscatter

Segmentation of Multibeam Echosounder Bathymetry and Backsca...

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OCEANS

作者： Jeremy Paul Coffelt Amos Smith Niklas Conen Peter Kampmann Institute for AI University of Bremen Bremen Germany Plan-Based Robot Control German Research Center for AI (DFKI) Osnabrück Germany Subsea Solutions ROSEN Creation Center Lingen Germany

ISBN: (数字)9798350362077

ISBN: (纸本)9798350362084

Multibeam echosounders (MBES) are the tool of choice for high-precision underwater surveys, especially when water conditions render optical imagery ineffective. We present and evaluate the following approaches for MBES segmentation: (1) real-time processing of single sounding profiles using traditional machine learning techniques, (2) batch processing of “waterfall” pseudo-images using a standard U-Net model, (3) the same model adapted to 2D projections of 3D point clouds, and (4) post-mission, survey-level processing using modern networks specifically designed for sparse point clouds. Strengths and weaknesses of the methods are discussed, including data preprocessing requirements, robustness, and ease of implementation/interpretation. Evaluation is performed on real data collected by an autonomous underwater vehicle (AUV) during a deep-sea industrial pipeline inspection.

关键词： Point cloud compression Solid modeling Three-dimensional displays Echo sounders Pipelines Neural networks Machine learning Inspection Engines Standards

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Detecting spatio-temporal Relations by Combining a Semantic Map with a Stream Processing Engine

Detecting spatio-temporal Relations by Combining a Semantic ...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Lennart Niecksch Henning Deeken Thomas Wiemann German Research Center for Artificial Intelligence (DFKI) DFKI Niedersachsen Plan-based Robot Control Group Osnabrück Germany Knowledge Based Systems and Autonomous Robotics groups Institute of Computer Science Osnabrück University Osnabrück Germany

Changes in topological spatial relations of objects are often strong indicators for state transitions in the underlying processes they are involved in. While various aspects of semantic mapping have been extensively researched, the reasoning about the temporal development of spatial relations of instances is often neglected. This paper presents a concept to combine a semantic map with a stream processing framework for live analysis of the spatio-temporal relation of objects, based on the map and information inferred from sensors streams. To demonstrate the functionality of our concept, we implemented a proof-of-concept system to track everyday events in an office environment. The presented application scenario clearly demonstrates the benefits of the proposed architecture for detecting and handling complex spatio-temporal events.

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Streamlined Acquisition of Large Sensor Data for Autonomous Mobile robots to Enable Efficient Creation and Analysis of Datasets

Streamlined Acquisition of Large Sensor Data for Autonomous ...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Mark Niemeyer Julian Arkenau Sebastian Pütz Joachim Hertzberg Plan-Based Robot Control Group DFKI Niedersachsen German Research Center for Artificial Intelligence Osnabrück Germany Nature Robots GmbH Osnabrück Germany Knowledge-Based Systems Group Osnabrück University Osnabrück Germany

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

The increasing usage of modern AI techniques represents a transforming shift in the robotics domain. Training and accessing new models requires substantial amounts of application-specific data, but the limited resources onboard mobile robots (like processing power, network bandwidth, etc.) pose a challenge for the development of efficient data recording and provisioning pipelines. Furthermore, accessing specific information based on a combination of spatial, temporal and semantic information is generally not supported by currently available tools. In this paper, we present a methodology which allows the efficient recording of robotic sensor data streams. We show that our approach reduces the overall time needed until the data can be served via the spatio-temporal-semantic query interface of the semantic environment representation SEEREP. We further present that the maximum sensor data rate which can be stored to disk in real-time is increased for large robotic data types like images and point clouds in comparison to frequently employed solutions within the ROS ecosystem.

关键词： Training Point cloud compression Semantics Pipelines Data acquisition Streaming media robot sensing systems

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Rmagine: 3D Range Sensor Simulation in Polygonal Maps via Ray Tracing for Embedded Hardware on Mobile robots

Rmagine: 3D Range Sensor Simulation in Polygonal Maps via Ra...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Alexander Mock Thomas Wiemann Joachim Hertzberg Knowledge Based Systems Group Institute of Computer Science Osnabrück University Osnabrück Germany Autonomous Robotics Group Institute of Computer Science Osnabrück University Osnabrück Germany Plan-based Robot Control DFKI Niedersachsen Osnabrück Germany

Sensor simulation has emerged as a promising and powerful technique to find solutions to many real-world robotic tasks like localization and pose tracking. However, commonly used simulators have high hardware requirements and are therefore used mostly on high-end computers. In this paper, we present an approach to simulate range sensors directly on embedded hardware of mobile robots that use triangle meshes as environment map. This library, called Rmagine, allows a robot to simulate sensor data for arbitrary range sensors directly on board via ray tracing. Since robots typically only have limited computational resources, Rmagine aims at being flexible and lightweight, while scaling well even to large environment maps. It runs on several platforms like Laptops or embedded computing boards like NVIDIA Jetson by putting an unified API over the specific proprietary libraries provided by the hardware manufacturers. This work is designed to support the future development of robotic applications depending on simulation of range data that could previously not be computed in reasonable time on mobile systems.

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FeatSense - A Feature-based Registration Algorithm with GPU-accelerated TSDF-Mapping Backend for NVIDIA Jetson Boards

arXiv

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arXiv 2023年

作者： Gaal, Julian Wiemann, Thomas Mock, Alexander Porrmann, Mario Institute of Computer Science Osnabrück University Osnabrück49069 Germany Fulda University of Applied Sciences Applied Computer Science Fulda36037 Germany Plan-based Robot Control Osnabrück49080 Germany

This paper presents FeatSense, a feature-based GPU-accelerated SLAM system for high resolution LiDARs, combined with a map generation algorithm for real-time generation of large Truncated Signed Distance Fields (TSDFs) on embedded hardware. FeatSense uses LiDAR point cloud features for odometry estimation and point cloud registration. The registered point clouds are integrated into a global Truncated Signed Distance Field (TSDF) representation. FeatSense is intended to run on embedded systems with integrated GPU-accelerator like NVIDIA Jetson boards. In this paper, we present a real-time capable TSDF-SLAM system specially tailored for close coupled CPU/GPU systems. The implementation is evaluated in various structured and unstructured environments and benchmarked against existing reference datasets. The main contribution of this paper is the ability to register up to 128 scan lines of an Ouster OS1-128 LiDAR at 10Hz on a NVIDIA AGX Xavier while achieving a TSDF map generation speedup by a factor of 100 compared to previous work on the same power budget. Copyright © 2023, The Authors. All rights reserved.

关键词： Embedded systems

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Mesh-based Object Tracking for Dynamic Semantic 3D Scene Graphs via Ray Tracing

arXiv

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arXiv 2024年

作者： Niecksch, Lennart Mock, Alexander Igelbrink, Felix Wiemann, Thomas Hertzberg, Joachim German Research Centre for Artificial Intelligence Plan-Based Robot Control Osnabrück Germany Osnabrück University Institute of Computer Science Knowledge-Based Systems Osnabrück Germany Fulda University of Applied Sciences Department of Applied Computer Science Robotics in Computer Science Fulda Germany

In this paper, we present a novel method for 3D geometric scene graph generation using range sensors and RGB cameras. We initially detect instance-wise keypoints with a YOLOv8s model to compute 6D pose estimates of known objects by solving PnP. We use a ray tracing approach to track a geometric scene graph consisting of mesh models of object instances. In contrast to classical point-to-point matching, this leads to more robust results, especially under occlusions between objects instances. We show that using this hybrid strategy leads to robust self-localization, pre-segmentation of the range sensor data and accurate pose tracking of objects using the same environmental representation. All detected objects are integrated into a semantic scene graph. This scene graph then serves as a front end to a semantic mapping framework to allow spatial reasoning. Copyright © 2024, The Authors. All rights reserved.

关键词： Mesh generation

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Comparison of UAV- and mowing machine-mounted LiDAR for grassland canopy height estimation 44

Comparison of UAV- and mowing machine-mounted LiDAR for gras...

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Die 44. Jahrestagung der Gesellschaft fur Informatik in der Land-, Forst- und Ernahrungswirtschaft 2024 - 44th Annual Conference of the German Association for Informatics in Agriculture, Forestry, and the Food Sector 2024

作者： Bracke, Justus Storch, Marcel Bald, Janis Jarmer, Thomas German Research Center for Artificial Intelligence Research Department Plan-based Robot Control Hamburger Straße 24 Osnabrück49084 Germany Osnabrück University Inst. of Computer Sc. Wachsbleiche 27 Osnabrück49090 Germany University of Applied Science Osnabrück Fac. of Agric. Sc. and Landscape Architecture Am Krümpel 31 Osnabrück49090 Germany

ISBN: (纸本)9783885797388

Towards autonomous process monitoring, canopy height estimation in grassland based on data from a mowing machine-mounted LiDAR and a UAV-LiDAR system is compared to manually measured ground truth heights. In a field trial, a LiDAR mounted on the cabin roof of the mowing machine recorded data during the mowing process, while two recording flights before and after the mowing were conducted with a UAV-LiDAR. The data from both systems were processed similarly and parameters such as height estimation method, spatial resolution and percentile filters were systematically varied to investigate their influence on height estimation accuracy. Statistical evaluation showed that canopy height estimates based on the UAV-LiDAR (R2 = 0.89, RMSE = 0.05 m) were more accurate and precise than those based on the mowing machine-mounted LiDAR (R2 = 0.51, RMSE = 0.08 m). The influence of the different investigated parameters varied. © 2024 Gesellschaft fur Informatik (GI). All rights reserved.

关键词： Lawn mowers

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