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27th RoboCup International Symposium, 2024

作者： Borse, Saurabh Viehmann, Tarik Ferrein, Alexander Lakemeyer, Gerhard The Mobile Autonomous Systems and Cognitive Robotics Institute FH Aachen University of Applied Science Aachen52066 Germany Knowledge-Based Systems Group RWTH Aachen University Aachen52074 Germany

ISBN: (纸本)9783031858581

The Robotino, developed by Festo Didactic, serves as a versatile platform in education and research for mobile robotics tasks. However, there currently is no ROS 2 integration for the Robotino available. In this paper we describe our work on a Webots simulation environment for a Robotino platform extended by LIDAR sensors. A ROS 2 integration and a pre-configured setup for localization and navigation using existing ROS packages from the Nav2 suite is provided. We validate our setup by comparing simulations with real-world experiments conducted by three Robotinos in a logistics environment in our lab. Additionally, we tested the setup using a ROS 2 hardware driver for the Robotino developed by team GRIPS of the RoboCup Logistics League. The results demonstrate the feasibility of using ROS 2 and Nav2 for navigation tasks on the Robotino platform showing great consistency between simulation and real-world performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Mobile robots

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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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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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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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A ROS~2-based Navigation and Simulation Stack for the Robotino

arXiv

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arXiv 2024年

The Robotino, developed by Festo Didactic, serves as a versatile platform in education and research for mobile robotics tasks. However, there currently is no ROS 2 integration for the Robotino available. In this paper we describe our work on a Webots simulation environment for a Robotino platform extended by LIght Detection And Ranging (LIDAR) sensors. A ROS 2 integration and a pre-configured setup for localization and navigation using existing ROS packages from the Nav2 suite is provided. We validate our setup by comparing simulations with real-world experiments conducted by three Robotinos in a logistics environment in our lab. Additionally, we tested the setup using a ROS 2 hardware driver for the Robotino developed by team GRIPS of the RoboCup Logistics League. The results demonstrate the feasibility of using ROS 2 and Nav2 for navigation tasks on the Robotino platform showing great consistency between simulation and real-world performance. © 2024, CC BY.

关键词： Optical radar

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Move Base Flex

Move Base Flex

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2018 IEEE/RSJ International Conference on Intelligent Robots and systems, IROS 2018

作者： Putz, Sebastian Santos Simon, Jorge Hertzberg, Joachim Osnabrück University Knowledge Based Systems Group Institute of Computer Science Wachsbleiche 27 Osnabruck49090 Germany Magazino GmbH Landsberger Str. 234 Munchen80687 Germany DFKI Robotics Innovation Center Osnabruck Branch Albert-Einstein-Str. 1 49076 Osnabruck Germany

ISBN: (纸本)9781538680940

We present Move Base Flex (MBF), a highly flexible, modular, map-independent, open-source navigation framework for use in ROS. MBF provides modular actions for executing plugins for path planning, motion control, and recovery. These actions define interfaces for external executives to allow highly flexible navigation strategies, which can be intertwined with other robot tasks. MBF has been successfully deployed in a professional setting at customer facilities to control robots in highly dynamic environments. We compare MBF with the well-known move-base and present the architecture as well as different deployment approaches, including how MBF can be used with different executives to perform complex navigation tasks interleaved with other robot operations. © 2018 IEEE.

关键词： Motion planning

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6D SLAM with approximate data association

6D SLAM with approximate data association

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International Conference on Advanced robotics (ICAR)

作者： A. Nuchter K. Lingemann J. Hertzberg H. Surmann Institute for Computer Science Knowledge-Based Systems Research Group University of Osnabrück Osnabruck Germany Fraunhofer Institute for Autonomous Intelligent System Sankt Augustin Germany

This paper provides a new solution to the simultaneous localization and mapping (SLAM) problem with six degrees of freedom. A fast variant of the iterative closest points (ICP) algorithm registers 3D scans taken by a mobile robot into a common coordinate system and thus provides relocalization. Hereby, data association is reduced to the problem of searching for closest points. Approximation algorithms for this searching, namely, approximate kd-trees and box decomposition trees, are presented and evaluated in this paper. A solution to 6D SLAM that considers all free parameters in the robot pose is built based on 3D scan matching

关键词： Simultaneous localization and mapping Robot kinematics Iterative closest point algorithm Laser modes Iterative algorithms Mobile robots Approximation algorithms Robot sensing systems robotics and automation Cloud computing

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Robust Object Detection at Regions of Interest with an Application in Ball Recognition

Robust Object Detection at Regions of Interest with an Appli...

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IEEE International Conference on robotics and Automation (ICRA)

作者： S. Mitri S. Frintrop K. Pervolz H. Surmann A. Nuchter Schloss Birlinghoven Fraunhofer Institute for Autonomous Intelligent System Sankt Augustin Germany Institute for Computer Science Knowledge-Based Systems Research Group University of Osnabrück Osnabruck Germany

In this paper, we present a new combination of a biologically inspired attention system (VOCUS – Visual Object detection with a CompUtational attention System) with a robust object detection method. As an application, we built a reliable system for ball recognition in the RoboCup context. Firstly, VOCUS finds regions of interest generating a hypothesis for possible locations of the ball. Secondly, a fast classifier verifies the hypothesis by detecting balls at regions of interest. The combination of both approaches makes the system highly robust and eliminates false detections. Furthermore, the system is quickly adaptable to balls in different scenarios: The complex classifier is universally applicable to balls in every context and the attention system improves the performance by learning scenario-specific features quickly from only a few training examples.

关键词： Robustness Object detection Application software Phase detection Classification tree analysis Intelligent systems Computational intelligence computer science knowledge based systems Biology

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Accurate object localization in 3D laser range scans

Accurate object localization in 3D laser range scans

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International Conference on Advanced robotics (ICAR)

作者： A. Nuchter K. Lingemann J. Hertzberg H. Surmann Institute for Computer Science Knowledge-Based Systems Research Group University of Osnabrück Osnabruck Germany Schloss Birlinghoven Fraunhofer Institute for Autonomous Intelligent System Sankt Augustin Germany

This paper presents a novel method for object detection and classification in 3D laser range data that is acquired by an autonomous mobile robot. Unrestricted objects are learned using classification and regression trees (CARTs) and using an Ada Boost learning procedure. Off-screen rendered depth and reflectance images serve as an input for learning. The performance of the classification is improved by combining both sensor modalities, which are independent from external light. This enables highly accurate, fast and reliable 3D object localization with point matching. Competitive learning is used for evaluating the accuracy of the object localization

关键词： Object detection Mobile robots Laser theory Regression tree analysis Rendering (computer graphics) computer science knowledge based systems Intelligent systems Intelligent robots Cognitive robotics

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HATSDF SLAM – Hardware-accelerated TSDF SLAM for Reconfigurable SoCs

HATSDF SLAM – Hardware-accelerated TSDF SLAM for Reconfigur...

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European Conference on Mobile Robots (ECMR)

作者： Marc Eisoldt Marcel Flottmann Julian Gaal Pascal Buschermöhle Steffen Hinderink Malte Hillmann Adrian Nitschmann Patrick Hoffmann Thomas Wiemann Mario Porrmann Computer Engineering 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

Simultaneous Localization and Mapping (SLAM) is one of the fundamental problems in autonomous robotics. Over the years, many approaches to solve this problem for 6D poses and 3D maps based on LiDAR sensors or depth cameras have been proposed. One of the main drawbacks of the solutions found in the literature is the required computational power and corresponding energy consumption. In this paper, we present an approach for LiDAR-based SLAM that maintains a global truncated signed distance function (TSDF) to represent the map. It is implemented on a System On Chip (SoC) with an integrated FPGA accelerator. The proposed system is able to track the position of a Velodyne VLP-16 LiDAR in real time, while maintaining a global TSDF map that can be used to create a polygonal map of the environment. We show that our implementation delivers competitive results compared to state-of-the-art algorithms while drastically reducing the power consumption compared to classical CPU or GPU-based methods.

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