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

作者： Daniel Seichter Benedict Stephan Söhnke Benedikt Fischedick Steffen Müller Leonard Rabes Horst-Michael Gross Neuroinformatics and Cognitive Robotics Lab Technische Universität Ilmenau Ilmenau Germany

As the application scenarios of mobile robots are getting more complex and challenging, scene understanding becomes increasingly crucial. A mobile robot that is supposed to operate autonomously in indoor environments must have precise knowledge about what objects are present, where they are, what their spatial extent is, and how they can be reached; i.e., information about free space is also crucial. Panoptic mapping is a powerful instrument providing such information. However, building 3D panoptic maps with high spatial resolution is challenging on mobile robots, given their limited computing capabilities. In this paper, we propose PanopticNDT – an efficient and robust panoptic mapping approach based on occupancy normal distribution transform (NDT) mapping. We evaluate our approach on the publicly available datasets Hypersim and ScanNetV2. The results reveal that our approach can represent panoptic information at a higher level of detail than other state-of-the-art approaches while enabling real-time panoptic mapping on mobile robots. Finally, we prove the real-world applicability of PanopticNDT with qualitative results in a domestic application.

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Estimation of Human Upper Body Orientation for Mobile robotics using an SVM Decision Tree on Monocular Images

Estimation of Human Upper Body Orientation for Mobile Roboti...

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IEEE/RSJ International Conference on Intelligent robotics and Systems

作者： Christoph Weinrich Christian Vollmer Horst-Michael Gross Neuroinformatics and Cognitive Robotics Lab Ilmenau University of Technology 98694 Ilmenau Germany

ISBN: (纸本)9781467317375

In this paper, we present a monocular, texture-based method for person detection and upper-body orientation classification. We build on a commonly used approach for person recognition that uses a Support Vector Machine (SVM) on Histograms of Oriented Gradients (HOG) [1] but replace the SVM by a decision tree with SVMs as binary decision makers. Thereby, in addition to the pure detection of persons, the distinction of eight upper-body orientation classes is enabled. The detection of humans and the estimation of their upper-body orientation from larger distances is essential for socially acceptable navigation of mobile robots. It permits to estimate the human's notice of the robot or even the human's interest in an interaction. Thus, it is the basis for the decision whether to approach or to avoid a human. By using an SVM decision tree for upper-body orientation estimation in discrete steps of 45°, we were able to classify about 64% of the test samples with an absolute error of less than 22.5°. This performance is much better than the results we obtained with comparable methods. Furthermore, our approach proved to be faster than the other state-of-the-art methods. This is of high relevance for implementation on mobile robots with limited computational resources.

关键词： SVM HOG mobile robots Support Vector Network Mobile robots Decision Trees Human absolute error

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Efficient Multi-Task Scene Analysis with RGB-D Transformers

arXiv

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arXiv 2023年

作者： Fischedick, Söhnke Benedikt Seichter, Daniel Schmidt, Robin Rabes, Leonard Gross, Horst-Michael Ilmenau University of Technology Neuroinformatics and Cognitive Robotics Lab Ilmenau98684 Germany

Scene analysis is essential for enabling autonomous systems, such as mobile robots, to operate in real-world environments. However, obtaining a comprehensive understanding of the scene requires solving multiple tasks, such as panoptic segmentation, instance orientation estimation, and scene classification. Solving these tasks given limited computing and battery capabilities on mobile platforms is challenging. To address this challenge, we introduce an efficient multi-task scene analysis approach, called EMSAFormer, that uses an RGB-D Transformer-based encoder to simultaneously perform the aforementioned tasks. Our approach builds upon the previously published EMSANet. However, we show that the dual CNN-based encoder of EMSANet can be replaced with a single Transformer-based encoder. To achieve this, we investigate how information from both RGB and depth data can be effectively incorporated in a single encoder. To accelerate inference on robotic hardware, we provide a custom NVIDIA TensorRT extension enabling highly optimization for our EMSAFormer approach. Through extensive experiments on the commonly used indoor datasets NYUv2, SUNRGB-D, and ScanNet, we show that our approach achieves state-of-the-art performance while still enabling inference with up to 39.1 FPS on an NVIDIA Jetson AGX Orin 32 GB. Copyright © 2023, The Authors. All rights reserved.

关键词： Signal encoding

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Robust Perception Skills for Autonomous Elevator Operation by Mobile Robots

Robust Perception Skills for Autonomous Elevator Operation b...

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European Conference on Mobile Robots (ECMR)

作者： Steffen Müller Benedict Stephan Tristan Müller Horst-Michael Gross Neuroinformatics and Cognitive Robotics Lab Technische Universität Ilmenau Ilmenau Germany

Autonomous mobile service robots with transportation tasks are often restricted to work on a single floor, since remote access to elevators is expensive to integrate for reasons of safety certification. Therefore, already ten years ago first robots have been enabled to use the human interface for riding an elevator. This requires a variety of perception and manipulation capabilities as well as social skills when it comes to interaction with other people who want to use the elevator too. We summarize the progress in solving the specific tasks of detecting and localizing the required buttons to press robustly. A deep-learning approach for detecting buttons in images is combined with a verification based on predefined knowledge on button arrangements in the elevator's control panels. Also perception of the elevator's state and our realization of the robot's elevator riding capabilities are discussed.

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R2D2 reloaded: Dynamic video projection on a mobile service robot

R2D2 reloaded: Dynamic video projection on a mobile service ...

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European Conference on Mobile Robots (ECMR)

作者： Ronny Stricker Steffen Müller Horst-Michael Gross Neuroinformatics and Cognitive Robotics Lab Technische Universität Ilmenau Ilmenau Germany

In this paper, we present a holistic approach to enable mobile robots using video projection in a situation aware and dynamic way. Therefore, we show how to autonomously detect wall segments that are suitable to be used as projection target in a dynamic environment. We derive several quality measures to score the wall segments found in the local environment and show how these scores can be used by a particle swarm optimization to find the best local projection position for the mobile robot. Furthermore, it is demonstrated how the presented approach can be used to display directions in an orientation training task for stroke patients while the robot is following them.

关键词： Image segmentation Trajectory Legged locomotion Detectors Cost function

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How Object Information Improves Skeleton-based Human Action Recognition in Assembly Tasks

arXiv

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arXiv 2023年

作者： Aganian, Dustin Köhler, Mona Baake, Sebastian Eisenbach, Markus Groß, Horst-Michael Ilmenau University of Technology Neuroinformatics and Cognitive Robotics Lab Ilmenau98684 Germany

As the use of collaborative robots (cobots) in industrial manufacturing continues to grow, human action recognition for effective human-robot collaboration becomes increasingly important. This ability is crucial for cobots to act autonomously and assist in assembly tasks. Recently, skeleton-based approaches are often used as they tend to generalize better to different people and environments. However, when processing skeletons alone, information about the objects a human interacts with is lost. Therefore, we present a novel approach of integrating object information into skeleton-based action recognition. We enhance two state-of-the-art methods by treating object centers as further skeleton joints. Our experiments on the assembly dataset IKEA ASM show that our approach improves the performance of these state-of-the-art methods to a large extent when combining skeleton joints with objects predicted by a state-of-the-art instance segmentation model. Our research sheds light on the benefits of combining skeleton joints with object information for human action recognition in assembly tasks. We analyze the effect of the object detector on the combination for action classification and discuss the important factors that must be taken into account. Copyright © 2023, The Authors. All rights reserved.

关键词： Object detection

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Efficient Multi-Task RGB-D Scene Analysis for Indoor Environments

arXiv

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arXiv 2022年

作者： Seichter, Daniel Fischedick, Söhnke Benedikt Köhler, Mona Groß, Horst-Michael Ilmenau University of Technology Neuroinformatics and Cognitive Robotics Lab Ilmenau 98684 Germany

Semantic scene understanding is essential for mobile agents acting in various environments. Although semantic segmentation already provides a lot of information, details about individual objects as well as the general scene are missing but required for many real-world applications. However, solving multiple tasks separately is expensive and cannot be accomplished in real time given limited computing and battery capabilities on a mobile platform. In this paper, we propose an efficient multi-task approach for RGB-D scene analysis (EMSANet) that simultaneously performs semantic and instance segmentation (panoptic segmentation), instance orientation estimation, and scene classification. We show that all tasks can be accomplished using a single neural network in real time on a mobile platform without diminishing performance - by contrast, the individual tasks are able to benefit from each other. In order to evaluate our multi-task approach, we extend the annotations of the common RGB-D indoor datasets NYUv2 and SUNRGB-D for instance segmentation and orientation estimation. To the best of our knowledge, we are the first to provide results in such a comprehensive multi-task setting for indoor scene analysis on NYUv2 and SUNRGB-D. Copyright © 2022, The Authors. All rights reserved.

关键词： Semantics

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Enhancing Person Perception for Mobile robotics by Real-Time RGB-D Person Attribute Estimation

Enhancing Person Perception for Mobile Robotics by Real-Time...

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IEEE/SICE International Symposium on System Integration

作者： Tim Wengefeld Daniel Seichter Benjamin Lewandowski Horst-Michael Gross Neuroinformatics and Cognitive Robotics Lab Technische Universität Ilmenau Ilmenau Germany

Person attribute estimation is a task of great importance for a variety of real-world robotic applications. While the computer vision community has made impressive progress over the last decade, they often rely on the sole use of RGB images from surveillance datasets and large deep- learning models without considering real-time requirements. By contrast, mobile robotic platforms have to deal with restricted resources but are often equipped with RGB-D cameras, offering complementary modalities. This paper presents an approach to robustly estimate soft-biometric attributes from full-body RGB-D appearances of persons in the surroundings of a mobile robot. The effects of depth, RGB and RGB-D data as input are analyzed, taking into account runtime and resource requirements. On the robotic attribute dataset SRL, it is shown that the presented approach outperforms other state-of-the-art approaches by a large margin. Furthermore, real-time requirements are met when applied on an NVIDIA Jetson AGX Xavier or even on a mobile CPU only. Finally, a previous system for person detection, upper body orientation estimation, and posture classification is integrated to enable an even more comprehensive perception of persons in the surroundings of a mobile robot in one joint approach. The source code for training and application will be made publicly available on GitHub.

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I see you lying on the ground — Can I help you? Fast fallen person detection in 3D with a mobile robot

I see you lying on the ground — Can I help you? Fast fallen...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Benjamin Lewandowski Tim Wengefeld Thomas Schmiedel Horst-Michael Gross Neuroinformatics and Cognitive Robotics Lab Technische Universität Ilmenau Ilmenau Germany

One important function in assistive robotics for home applications is the detection of emergency cases, like falls. In this paper, we present a new detection system which can run on a mobile robot to detect persons after a fall event robustly. The system is based on 3D Normal Distributions Transform (NDT) maps on which a powerful segmentation is applied. Segments most likely belonging to a person lying on the ground are grouped into clusters. After extracting features with a soft encoding approach, each cluster is classified separately. Our experiments show that the system is able to reliably detect fallen persons in real-time. It clearly outperforms other 3D state-of-the-art approaches. We can show that our system is able to handle even very challenging situations, where fallen persons are very close to other objects in the apartment. Such complex fall events often occur in real-world applications.

关键词： Feature extraction Three-dimensional displays Robot sensing systems Detectors Iron Histograms

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Efficient and Robust Semantic Mapping for Indoor Environments

Efficient and Robust Semantic Mapping for Indoor Environment...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Daniel Seichter Patrick Langer Tim Wengefeld Benjamin Lewandowski Dominik Hö chemer Horst-Michael Gross Neuroinformatics and Cognitive Robotics Lab Technische Universit&#x00E4 t Ilmenau Ilmenau Germany

A key proficiency an autonomous mobile robot must have to perform high-level tasks is a strong understanding of its environment. This involves information about what types of objects are present, where they are, what their spatial extend is, and how they can be reached, i.e., information about free space is also crucial. Semantic maps are a powerful instrument providing such information. However, applying semantic segmentation and building 3D maps with high spatial resolution is challenging given limited resources on mobile robots. In this paper, we incorporate semantic information into efficient occupancy normal distribution transform (NDT) maps to enable real-time semantic mapping on mobile robots. On the publicly available dataset Hypersim, we show that, due to their sub-voxel accuracy, semantic NDT maps are superior to other approaches. We compare them to the recent state-of-the-art approach based on voxels and semantic Bayesian spatial kernel inference (S-BKI) and to an optimized version of it derived in this paper. The proposed semantic NDT maps can represent semantics to the same level of detail, while mapping is 2.7 to 17.5 times faster. For the same grid resolution, they perform significantly better, while mapping is up to more than 5 times faster. Finally, we prove the real-world applicability of semantic NDT maps with qualitative results in a domestic application.

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