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

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

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

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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

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

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

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

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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

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

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

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

Monocular Localization in Feature-Annotated 3D Polygon Maps

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Monocular Localization in Feature-Annotated 3D Polygon Maps

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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Rmagine: 3D Range Sensor Simulation in Polygonal Maps via Raytracing for Embedded Hardware on Mobile Robots

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arXiv 2022年

作者： Mock, Alexander Wiemann, Thomas Hertzberg, Joachim Knowledge Based Systems Group Institute of Computer Science Osnabrück University Berghoffstraße 11 Osnabrück49090 Germany Autonomous Robotics Group Institute of Computer Science Osnabrück University Berghoffstraße 11 Osnabrück49090 Germany DFKI Niedersachsen Plan-based Robot Control Berghoffstraße 11 Osnabrück49090 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 raytracing. Since robots typically only have limited computational resources, the 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. Copyright © 2022, The Authors. All rights reserved.

关键词： Mobile robots

HATSDF SLAM – Hardware-accelerated TSDF SLAM for Reconfigurable SoCs

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

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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MICP-L: Mesh-based ICP for Robot Localization using Hardware-Accelerated Ray Casting

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arXiv 2022年

作者： Mock, Alexander Wiemann, Thomas Pütz, Sebastian Hertzberg, Joachim Knowledge-Based Systems Group Institute of Computer Science Osnabrück University Hamburger Straße 24 Osnabrück49084 Germany DFKI Robotics Innovation Center Osnabrück Branch Hamburger Straße 24 Osnabrück49084 Germany Fulda University of Applied Sciences Fulda Germany

关键词： Motion planning