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IEEE-RAS 21st International Conference on Humanoid robots (Humanoids)

作者： Fabisch, Alexander Uliano, Manuela Marschner, Dennis Laux, Melvin Brust, Johannes Controzzi, Marco DFKI GmbH Robot Innovat Ctr Robert Hooke Str 1 D-28359 Bremen Germany Scuola Super Sant Anna BioRobot Inst Pisa Italy Scuola Super Sant Anna Dept Excellence Robot & AI Pisa Italy Univ Bremen Robot Res Grp Bremen Germany DFKI GmbH Plan Based Robot Control Bremen Germany

ISBN: (纸本)9798350309799

Manipulating objects with robotic hands is a complicated task. Not only the fingers of the hand, but also the pose of the robot's end effector need to be coordinated. Using human demonstrations of movements is an intuitive and data-efficient way of guiding the robot's behavior. We propose a modular framework with an automatic embodiment mapping to transfer recorded human hand motions to robotic systems. In this work, we use motion capture to record human motion. We evaluate our approach on eight challenging tasks, in which a robotic hand needs to grasp and manipulate either deformable or small and fragile objects. We test a subset of trajectories in simulation and on a real robot and the overall success rates of about 70% are aligned.

关键词： End effectors

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The Mesh Tools Package - Introducing Annotated 3D Triangle Maps in ROS

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robotICS AND AUTONOMOUS SYSTEMS 2021年 138卷 103688-103688页

作者： Puetz, Sebastian Wiemann, Thomas Hertzberg, Joachim Osnabruck Univ Knowledge Based Syst Grp Osnabruck Germany Osnabruck Univ German Res Ctr Artificial Intelligence DFICI Plan Based Robot Control Osnabruck Germany

Triangle mesh maps for robotic applications are becoming increasingly popular, but are not yet effectively supported in the robot Operating System (ROS). We introduce the Mesh Tools package consisting of message definitions, RViz plugins and tools, as well as a persistence layer. These tools make annotated triangle maps available in ROS and allow to publish, edit and inspect such maps within the existing ROS software stack. The persistence layer efficiently loads and stores large mesh maps. The proposed plugins and tools enable the visualization and validation of the complete layered map and associated properties to allow fluid interaction. We demonstrate the seamless integration of our tools in two application areas as a proof-of-concept: Labeling of triangle clusters for semantic mapping and robot navigation on triangle meshes in rough terrain outdoor environments by integrating our tools into an existing navigation stack. (C) 2020 Elsevier B.V. All rights reserved.

关键词： ROS Triangle meshes RViz 3D mesh navigation

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Segmentation of Multibeam Echosounder Bathymetry and Backscatter

Segmentation of Multibeam Echosounder Bathymetry and Backsca...

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OCEANS Conference

作者： Coffelt, Jeremy Paul Smith, Amos Conen, Niklas Kampmann, Peter Univ Bremen Inst AI Bremen Germany German Res Ctr AI DFKI Plan Based Robot Control Osnabruck Germany ROSEN Creat Ctr Subsea Solut Lingen Germany ROSEN Creat Ctr Subsea Solut Bremen Germany

ISBN: (纸本)9798350362077

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： underwater perception image and cloud segmentation multibeam sonar marine robots pipeline inspection

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Self-supervised Detection and Pose Estimation of Logistical Objects in 3D Sensor Data 25

Self-supervised Detection and Pose Estimation of Logistical ...

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25th International Conference on Pattern Recognition (ICPR)

作者： Mueller, Nikolas Stenzel, Jonas Chen, Jian-Jia Plan Based Robot Control German Res Ctr Artificia Osnabruck Germany Fraunhofer Inst Mat Flow & Logist Automat & Embedded Syst Dortmund Germany Tech Univ Dortmund Inst Comp Sci Design Automat Embedded Syst Dortmund Germany

ISBN: (纸本)9781728188089

Localization of objects in cluttered scenes with machine learning methods is a fairly young research area. Despite the high potential of object localization for full process automation in Industry 4.0 and logistical environments, 3D data sets for such applications to train machine learning models are not openly available and only few publications have been made on that topic. To the authors knowledge, this is the first publication that describes a self-supervised and fully automated deep learning approach for object pose estimation using simulated 3D data. The solution covers the simulated generation of training data, the detection of objects in point clouds using a fully convolutional voting network and the computation of the pose for each detected object instance.

关键词： object detection pose estimation computer vision pattern recognition 3D vision learning-based vision

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A fully integrated system for hardware-accelerated TSDF SLAM with LiDAR sensors (HATSDF SLAM)

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robotICS AND AUTONOMOUS SYSTEMS 2022年第0期156卷

作者： Eisoldt, Marc Gaal, Julian Wiemann, Thomas Flottmann, Marcel Rothmann, Marc Tassemeier, Marco Porrmann, Mario Osnabruck Univ Autonomous Robot Grp Osnabruck Germany Osnabruck Univ Comp Engn Grp Osnabruck Germany DFKI Lab Niedersachsen Plan Based Robot Control Grp Osnabruck 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 state-of-the-art LiDARs 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.(c) 2022 Elsevier B.V. All rights reserved.

关键词： SLAM Hardware acceleration FPGA programming 3D mapping

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FeatSense - A Feature-based Registration Algorithm with GPU-accelerated TSDF-Mapping Backend for NVIDIA Jetson Boards 17

FeatSense - A Feature-based Registration Algorithm with GPU-...

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7th IEEE International Conference on robotic Computing (IRC)

作者： Gaal, Julian Wiemann, Thomas Mock, Alexander Porrmann, Mario Osnabruck Univ Inst Comp Sci D-49069 Osnabruck Germany Fulda Univ Appl Sci Appl Comp Sci D-36037 Fulda Germany German Res Ctr Artificial Intelligence DFKI Plan Based Robot Control D-49080 Osnabruck Germany

ISBN: (纸本)9798350395747

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 closely 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.

关键词： Embedded systems

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ReconfROS: An approach for accelerating ROS nodes on reconfigurable SoCs

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MICROPROCESSORS AND MICROSYSTEMS 2022年 94卷

作者： Eisoldt, Marc Flottmann, Marcel Gaal, Julian Hinderink, Steffen Vana, Juri Tassemeier, Marco Rothmann, Marc Wiemann, Thomas Porrmann, Mario Osnabruck Univ Comp Engn & Autonomous Robot Grp Inst Comp Sci Osnabruck Germany German Res Ctr Artificial Intelligence DFKI Nieder Plan based Robot Control Grp Osnabruck Germany

In this paper, we present an approach to integrate reconfigurable SoCs into the well known robot Operating System (ROS) called ReconfROS. Our method allows to implement hardware-accelerated algorithms on FPGAs and integrates them directly into the ROS ecosystem. This allows to combine the established and well tested ROS infrastructure together with low-power hardware acceleration. As a proof-of-concept for this novel integration, we ported an existing image-based path-following algorithm onto an FPGA and tested it on an unmanned ground vehicle (UGV). Evaluations utilizing the FPGA-accelerator directly synthesized from C-code show a three times performance improvement combined with a 15 times reduction of energy per frame. The approach can be easily ported to different hardware platforms based on Xilinx reconfigurable SoCs.

关键词： ROS FPGA SoC UGV UAV Path detection

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Continuous Shortest Path Vector Field Navigation on 3D Triangular Meshes for Mobile robots

Continuous Shortest Path Vector Field Navigation on 3D Trian...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Sebastian Pütz Thomas Wiemann Malte Kleine Piening Joachim Hertzberg Knowledge Based Systems Institute of Computer Science Osnabrück University Plan-Based Robot Control German Research Center for Artificial Intelligence DFKI

We present a highly efficient approach to compute continuous shortest path vector fields on arbitrarily shaped 3D triangular meshes for robot navigation in complex real-world outdoor environments. The continuity of the vector field allows to query the shortest distance, direction and geodesic path to the goal at any point within the mesh triangles, resulting in accurate paths. In order to avoid impassable areas, our wavefront propagation method runs on a modular extendable multilayer map architecture taking different geometric cost layers into account. We describe the mathematical foundation of the geodesic distances and continuous vector field computation and demonstrate the performance in real-world and multilevel environments on our campus with a tunnel, ramps and stair- cases, and in a difficult, steep forest area with a stone quarry. For reproducibility, we provide a ready-to-use ROS software stack as well as Gazebo simulations.

关键词： Three-dimensional displays Runtime Costs Navigation robot kinematics Computational modeling Forestry

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

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

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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.

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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

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