检索结果-内蒙古大学图书馆

43. Jahrestagung der Gesellschaft fur Informatik in der Land-, Forst- und Ernahrungswirtschaft - Resiliente Agri-Food-Systeme: Herausforderungen und Losungsansatze, GIL 2023 - 43rd Annual Conference of the Society for Informatics in Agriculture, Forestry, and Food Industry - Resilient Agri-Food Systems: Challenges and Solutions, GIL 2023

作者： Renz, Marian Niemeyer, Mark Hertzberg, Joachim German Research Center for Artificial Intelligence Plan-Based Robot Control Group Berghoffstr. 11 Osnabrück49090 Germany Osnabrück University Knowledge-Based Systems Group Wachsbleiche 27 Osnabrück49090 Germany

ISBN: (纸本)9783885797241

Weeds are commonly known as a major factor for yield losses in agriculture, competing with crops for resources like nutrients, water, and light. However, keeping specific weeds could benefit agricultural sites for example by nitrogen fixation, erosion protection, or increasing biodiversity. This comes with technological challenges like plant detection and classification, damage estimation, and selective removal. This paper presents a model-based approach to the problem of damage estimation of perceived plants. The system uses contextual and background knowledge in the form of rules about the plant count per square meter and the distance to the nearest crop together with thresholds for each weed species. The functionality is demonstrated using an artificial dataset and exemplary thresholds, showing the potential of using knowledge about plant-crop interactions for more sophisticated weed control systems. © 2023 Gesellschaft fur Informatik (GI). All rights reserved.

关键词： Crops

来源：评论

学校读者我要写书评

暂无评论

The pitfalls of transfer learning in computer vision for agriculture 42

The pitfalls of transfer learning in computer vision for agr...

引用

42. Jahrestagung 2022 der Gesellschaft fur Informatik in der Land-, Forst- und Ernahrungswirtschaft: Was bedeutet Kunstliche Intelligenz fur die Agrar- und Ernahrungswirtschaft, GIL 2022 - 42nd Annual Conference 2022 of the Society for Information Technology in Agriculture, Forestry and Food Industry: What does Artificial Intelligence mean for the Agricultural and Food industry, GIL 2022

作者： Autz, Julius Mishra, Saurabh Kumar Herrmann, Lena Hertzberg, Joachim DFKI Labor Niedersachsen Plan Based Robot Control Osnabrück Germany Osnabrück University KBS Group Osnabrück Germany

ISBN: (纸本)9783885797111

Computer vision applications based on modern AI methods are becoming increasingly important in agriculture, supporting and automating common processes. These applications are usually based on well-established architectures and pre-trained models. However, our prior experience has shown that applying the concept of transfer learning to AI tasks in agriculture repeatedly resulted in systematic issues. The structure of agricultural images, containing objects similar in shape, color and texture, makes the reuse of well-established applications more challenging. To give a more detailed insight into the expected challenges, we trained two different networks, which are well-established in the literature: Mask R-CNN and YOLOv5 [He18;Jo21] and investigated them in two different learning setups. First, we applied the concept of transfer learning to these models by pre-training each on the COCO dataset and subsequently continued expanding the available target set with classes of the sugar beets dataset [Ch17]. In the second setup, we skipped pre-training and only trained the models on the given agriculture dataset. Furthermore, we describe the reasons for the results in more detail and highlight possible causes for the identified differences. Finally, the different performances of the networks allowed us to improve on best practices for the agricultural domain and give some advice for future computer vision tasks in this area. © 2022 Gesellschaft fur Informatik (GI). All rights reserved.

关键词： Computer vision

来源：评论

学校读者我要写书评

暂无评论

Scalable Evaluation Pipeline of CNN-based Perception for robotic Sensor Data Under Different Environment Conditions

Scalable Evaluation Pipeline of CNN-Based Perception for Rob...

引用

European Conference on Mobile robots (ECMR)

作者： Naeem Iqbal Mark Niemeyer Jan Christoph Krause Joachim Hertzberg Plan-Based Robot Control Group DFKI Niedersachsen German Research Center for Artificial Intelligence Osnabrück Germany Knowledge-Based Systems Group Osnabrück University Osnabrück Germany

Deep learning impacted a wide variety of perception applications for autonomous mobile robots. In classic computer vision benchmark tests, new algorithms keep appearing that outperform each other. However, these benchmark tests cannot be generalized, so that the specific application must be considered for the selection of sensors and algorithms. Especially in the agricultural domain, environmental conditions like weather and vegetation significantly influence the reliability of sensor systems. Therefore, it is essential to test different sensor modalities and algorithms in the operational design domain. This motivates the need for an evaluation framework which has the flexibility to compare and validate various perception algorithms, sensors suites, and data samples with a focus on different conditions. This paper proposes a pipeline combining a test environment (AI-TEST-FIELD), a semantic environment representation (SEEREP), and an inference server (Triton) for an automatic evaluation of different CNN-based perception algorithms under various environment conditions. Recurring and comparable recordings of raw sensor data with identical scenarios and objects can be performed on the test field, with the only difference being the environmental conditions. The inference results are inferred once and stored alongside the sensor data in SEEREP. Thus, they can be queried efficiently based on the environment conditions to generate (partially overlapping) subsets of the whole dataset. It is demonstrated how this pipeline can be used to apply the CNN-inference just once on the data, and how the queried subsets can subsequently be used to evaluate the performance in different environment conditions.

关键词：

来源：评论

学校读者我要写书评

暂无评论

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 ...

引用

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

来源：评论

学校读者我要写书评

暂无评论

RadaRays: Real-time Simulation of Rotating FMCW Radar for Mobile robotics via Hardware-accelerated Ray Tracing

arXiv

引用

arXiv 2023年

作者： Mock, Alexander Magnusson, Martin Hertzberg, Joachim The KBS group of Osnabrück University Germany The Plan-based Robot Control Group DFKI Niedersachsen Germany The RNP lab The AASS Research Centre Orebro University Sweden

RadaRays allows for the accurate modeling and simulation of rotating FMCW radar sensors in complex environments, including the simulation of reflection, refraction, and scattering of radar waves. Our software is able to handle large numbers of objects and materials in real-time, making it suitable for use in a variety of mobile robotics applications. We demonstrate the effectiveness of RadaRays through a series of experiments and show that it can more accurately reproduce the behavior of FMCW radar sensors in a variety of environments, compared to the ray casting-based lidar-like simulations that are commonly used in simulators for autonomous driving such as CARLA. Our experiments additionally serve as a valuable reference point for researchers to evaluate their own radar simulations. By using RadaRays, developers can significantly reduce the time and cost associated with prototyping and testing FMCW radar-based algorithms. We also provide a Gazebo plugin that makes our work accessible to the mobile robotics community. Copyright © 2023, The Authors. All rights reserved.

关键词： robot programming

来源：评论

学校读者我要写书评

暂无评论

Online Knowledge Integration for 3d Semantic Mapping: A Survey

SSRN

引用

SSRN 2025年

作者： Igelbrink, Felix Renz, Marian Günther, Martin Powell, Piper Niecksch, Lennart Lima, Oscar Atzmueller, Martin Hertzberg, Joachim German Research Center for Artificial Intelligence Research Department Plan-based Robot Control Osnabrück Germany Osnabrück University Institute of Computer Science Knowledge-Based Systems Group Osnabrück Germany Osnabrück University Institute of Computer Science Semantic Information Systems Group Osnabrück Germany

Semantic mapping is a key component of robots operating in and interacting with objects in structured environments. Traditionally, geometric and knowledge representations within a semantic map have only been loosely integrated. However, recent advances in deep learning now allow full integration of prior knowledge, represented as knowledge graphs or language concepts, into sensor data processing and semantic mapping pipelines. Semantic scene graphs and language models enable modern semantic mapping approaches to incorporate graph-based prior knowledge or to leverage the rich information in human language both during and after the mapping process. This has sparked substantial advances in semantic mapping, leading to previously impossible novel applications. This survey reviews these recent developments comprehensively, with a focus on online integration of knowledge into semantic mapping. We specifically focus on methods using semantic scene graphs for integrating symbolic prior knowledge and language models for respective capture of implicit common-sense knowledge and natural language concepts. © 2025, The Authors. All rights reserved.

关键词： Knowledge graph

来源：评论

学校读者我要写书评

暂无评论

Online Knowledge Integration for 3D Semantic Mapping: A Survey

arXiv

引用

arXiv 2024年

关键词： Knowledge graph

来源：评论

学校读者我要写书评

暂无评论

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...

引用

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.

关键词：

来源：评论

学校读者我要写书评

暂无评论

MICP-L: Mesh-based ICP for robot Localization Using Hardware-Accelerated Ray Casting

MICP-L: Mesh-based ICP for Robot Localization Using Hardware...

引用

IEEE/RSJ International Conference on Intelligent robots and Systems (IROS)

作者： Alexander Mock Thomas Wiemann Sebastian Pütz Joachim Hertzberg Knowledge-Based Systems Group Institute of Computer Science Os-nabrück University Osnabrück Germany Robotics in Computer Science Fulda University of Applied Sciences Fulda Germany Nature Robots GmbH Osnabruck Germany Plan-Based Robot Control DFKI Os-nabrück Germany

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Triangle mesh maps are a versatile 3D environment representation for robots to navigate in challenging indoor and outdoor environments exhibiting tunnels, hills and varying slopes. To make use of these mesh maps, methods are needed to accurately localize robots in such maps to perform essential tasks like path planning and navigation. We present Mesh ICP Localization (MICP-L), a novel and computationally efficient method for registering one or more range sensors to a triangle mesh map to continuously localize a robot in 6D, even in GPS-denied environments. We accelerate the computation of ray casting correspondences (RCC) between range sensors and mesh maps by supporting different parallel computing devices like multicore CPUs, GPUs and the latest NVIDIA RTX hardware. By additionally transforming the covariance computation into a reduction operation, we can optimize the initial guessed poses in parallel on CPUs or GPUs, making our implementation applicable in real-time on many architectures. We demonstrate the robustness of our localization approach with datasets from agricultural, aerial, and automotive domains.

关键词： Location awareness Casting Three-dimensional displays Navigation Computer architecture robot sensing systems Sensors Software reliability State estimation robots

来源：评论

学校读者我要写书评

暂无评论

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

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

引用

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.

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：