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arXiv 2021年

作者： Pfreundschuh, Patrick Hendrikx, Hubertus F.C. Reijgwart, Victor Dubé, Renaud Siegwart, Roland Cramariuc, Andrei Autonomous Systems Lab ETH Zürich Switzerland Sevensense Robotics AG

Highly dynamic environments, with moving objects such as cars or humans, can pose a performance challenge for LiDAR SLAM systems that assume largely static scenes. To overcome this challenge and support the deployment of robots in real world scenarios, we propose a complete solution for a dynamic object aware LiDAR SLAM algorithm. This is achieved by leveraging a real-time capable neural network that can detect dynamic objects, thus allowing our system to deal with them explicitly. To efficiently generate the necessary training data which is key to our approach, we present a novel end-to-end occupancy grid based pipeline that can automatically label a wide variety of arbitrary dynamic objects. Our solution can thus generalize to different environments without the need for expensive manual labeling and at the same time avoids assumptions about the presence of a predefined set of known objects in the scene. Using this technique, we automatically label over 12000 LiDAR scans collected in an urban environment with a large amount of pedestrians and use this data to train a neural network, achieving an average segmentation IoU of 0.82. We show that explicitly dealing with dynamic objects can improve the LiDAR SLAM odometry performance by 39.6% while yielding maps which better represent the environments. A supplementary video1 as well as our test data2 are available online. Copyright © 2021, The Authors. All rights reserved.

关键词： Optical radar

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A Framework for Collaborative Multi-Robot Mapping using Spectral Graph Wavelets

arXiv

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arXiv 2022年

作者： Bernreiter, Lukas Khattak, Shehryar Ott, Lionel Siegwart, Roland Hutter, Marco Cadena, Cesar Autonomous Systems Lab. ETH Zurich Switzerland Robotics Systems Lab. ETH Zurich Switzerland

The exploration of large-scale unknown environments can benefit from the deployment of multiple robots for collaborative mapping. Each robot explores a section of the environment and communicates onboard pose estimates and maps to a central server to build an optimized global multi-robot map. Naturally, inconsistencies can arise between onboard and server estimates due to onboard odometry drift, failures, or degeneracies. The mapping server can correct and overcome such failure cases using computationally expensive operations such as inter-robot loop closure detection and multi-modal mapping. However, the individual robots do not benefit from the collaborative map if the mapping server provides no feedback. Although server updates from the multi-robot map can greatly alleviate the robotic mission strategically, most existing work lacks them, due to their associated computational and bandwidth-related costs. Motivated by this challenge, this paper proposes a novel collaborative mapping framework that enables global mapping consistency among robots and the mapping server. In particular, we propose graph spectral analysis, at different spatial scales, to detect structural differences between robot and server graphs, and to generate necessary constraints for the individual robot pose graphs. Our approach specifically finds the nodes that correspond to the drift’s origin rather than the nodes where the error becomes too large. We thoroughly analyze and validate our proposed framework using several real-world multi-robot field deployments where we show improvements of the onboard system up to 90% and can recover the onboard estimation from localization failures and even from the degeneracies within its estimation. Copyright © 2022, The Authors. All rights reserved.

关键词： Industrial robots

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Mechatronic Design of a Human Robot Coupling Interface for Teaching by Demonstration

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IFAC-PapersOnLine 2016年第21期49卷 675-680页

作者： Liang P. Yang C. Wang N. Li Z. Li R. State Key Lab of Robotics Systems Harbin Institute of Technology Harbin China Zienkiewicz Centre for Computational Engineering Swansea University Swansea United Kingdom Centre for Robotics and Neural Systems Plymouth University United Kingdom The MOE Key Laboratory of Autonomous System and Network Control College of Automation Science and Engineering South China University of Technology Guangzhou 510640 China

In this paper, we have developed a mechatronic human-robot coupling interface for kinaesthetic guidance based human robot teaching interface, by integration of a 6-axis force sensor, a coupling mechanism and wearable electromyography (EMG) arrays. Our design aims to transfer human operator's arm kinematics, force as well as human upper limb stiffness estimated from EMG signals to robot manipulators. Experiments have been carried out to test the teaching by demonstration performance of the coupling system in two scenarios: i) human teaching a robot for lifting up task; ii) human-robot skill transfer for one joint motion. Experimental results indicated that the coupling design nearly caused no disturbance to human upper limb stiffness estimation. Moreover, human arm kinematics and force/torque in task space can be transferred to the robot in a natural way with guaranteed safety. This coupling interface design can be easily extended to other robot platforms and has great potentials in application of human robot skill transfer. © 2016

关键词： electromyography (EMG) human robot coupling teaching by demonstration

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Meta-Cognition-Based Simple And Effective Approach To Object Detection

Meta-Cognition-Based Simple And Effective Approach To Object...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Sannidhi P Kumar Chandan Gautam Suresh Sundaram WIRIN Autonomous Systems and Robotics Lab Indian Institute of Science Bangalore Karnataka

Recently, many researchers have attempted to improve deep learning-based object detection models, both in terms of accuracy and operational speeds. However, frequently, there is a trade-off between speed and accuracy of such models, which encumbers their use in practical applications such as autonomous navigation. In this paper, we explore a meta-cognitive learning strategy for object detection to improve generalization ability while at the same time maintaining detection speed. The meta-cognitive method selectively samples the object instances in the training dataset to reduce overfitting. We use YOLO v3 Tiny as a base model for the work and evaluate the performance using the MS COCO dataset. The experimental results indicate an improvement in absolute precision of 2.6% (minimum), and 4.4% (maximum), with no overhead to inference time.

关键词： Training Image resolution Conferences Object detection Signal processing Acoustics Speech processing

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Navigation planning for legged robots in challenging terrain

Navigation planning for legged robots in challenging terrain

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IEEE/RSJ International Conference on Intelligent Robots and systems

作者： Martin Wermelinger Peter Fankhauser Remo Diethelm Philipp Krusi Roland Siegwart Marco Hutter Robotics Systems Lab (RSL) ETH Zurich Switzerland Autonomous Systems Lab (ASL) ETH Zurich Switzerland

ISBN: (纸本)9781509037636

This paper presents a framework for planning safe and efficient paths for a legged robot in rough and unstructured terrain. The proposed approach allows to exploit the distinctive obstacle negotiation capabilities of legged robots, while keeping the complexity low enough to enable planning over considerable distances in short time. We compute typical terrain characteristics such as slope, roughness, and steps to build a traversability map. This map is used to assess the costs of individual robot footprints as a function of the robot-specific obstacle negotiating capabilities for steps, gaps and stairs. Our sampling-based planner employs the RRT* algorithm to optimize path length and safety. The planning framework has a hierarchical architecture to frequently replan the path during execution as new terrain is perceived with onboard sensors. Furthermore, a cascaded planning structure makes use of different levels of simplification to allow for fast search in simple environments, while retaining the ability to find complex solutions, such as paths through narrow passages. The proposed navigation planning framework is integrated on the quadrupedal robot StarlETH and extensively tested in simulation as well as on the real platform.

关键词： Planning Legged locomotion Robot sensing systems Navigation Computer architecture

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Visual odometry based on the Fourier-Mellin transform for a rover using a monocular ground-facing camera

Visual odometry based on the Fourier-Mellin transform for a ...

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IEEE International Conference on Mechatronics

作者： Kazik, Tim Goktogan, Ali Haydar Autonomous Systems Lab Swiss Federal Institute of Technology Zurich Switzerland Australian Centre for Field Robotics School of Aerospace Mechanical and Mechatronic Eng. University of Sydney Sydney NSW 2006 Australia

ISBN: (纸本)9781612849836

This paper presents a visual odometry method that estimates the location and orientation of a robotic rover platform. The visual odometry approach is based on Fourier-Mellin transforms and phase-only matched niters which extract similarity transform parameters (i.e. rotation, scaling and translation) between consecutive images captured by a ground-pointing camera. By mapping these inter-image frame parameters to the real world, the rover's location is recovered. Given that the proposed strategy is a global method, it is well suited for environments exhibiting few features and illumination changes. Results show that this approach is more robust and accurate than an optical flow based visual odometry method. © 2011 IEEE.

关键词： Cameras

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Credible Online Dynamics Learning for Hybrid UAVs

Credible Online Dynamics Learning for Hybrid UAVs

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IEEE International Conference on robotics and Automation (ICRA)

作者： David Rohr Nicholas Lawrance Olov Andersson Roland Siegwart Autonomous Systems Lab ETH Zurich Zurich Switzerland Robotics and Autonomous Systems Group CSIRO Data61 QLD Australia

Hybrid unmanned aerial vehicles (H-UAVs) are highly versatile platforms with the ability to transition between rotary- and fixed-wing flight. However, their (aero)dynamics tend to be highly nonlinear which increases the risk of introducing safety-critical modeling errors in a controller. Designing a safe, yet not too cautious controller, requires a credible model which provides accurate dynamics uncertainty quantification. We present a data-efficient, probabilistic semi-parametric dynamics modeling approach that allows for online, filter-based inference. The proposed model leverages prior knowledge using a nominal parametric model, and combines it with residuals in the form of sparse Gaussian processes to account for possibly unmodeled forces and moments. Uncertain nominal and residual parameters are jointly estimated using Bayesian filtering. The resulting model accuracy and the reliability of its predicted uncertainty are analyzed for both a simulated and a real example, where we learn the 6DoF nonlinear dynamics of a tiltwing H-UAV from a few minutes of flight data. Compared to a residual-free nominal model, the proposed semi-parametric approach provides increased model accuracy in relevant parts of the flight envelope and substantially higher credibility overall.

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Optimizing Vehicle Motion Control for Generating Multiple Sensations

Optimizing Vehicle Motion Control for Generating Multiple Se...

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2018 IEEE Intelligent Vehicles Symposium, IV 2018

作者： Kissai, Moad Mouton, Xavier Monsuez, Bruno Martinez, DIdier Tapus, Adriana Department of Computer and System Engineering ENSTA ParisTech Autonomous Systems and Robotics Lab 828 Boulevard des Marchaux Palaiseau Cedex91762 France Chassis Systems Department Group Renault 1 Avenue du Golf Guyancourt78280 France

ISBN: (纸本)9781538644522

Most of automotive researches focus on autonomous vehicles. Studies regarding trajectory planning and trajectory tracking became preponderant. As in case of commercial ground vehicles there is a driver in the loop, one should raise the important question of how the trajectory should be tracked. In this paper, we investigate the influence of controlling integrated chassis systems on the vehicle's behavior. A fixed Model Predictive Control is used to track the trajectory. Tunable vehicle motion control is however used to provide different motion feelings. Results show that a specific trajectory could be followed in different manners. Therefore, vehicle dynamics can be and should be controlled in such a way to generate adaptive trust feelings to passengers in case of autonomous driving. © 2018 IEEE.

关键词： Trajectories

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Real-time dense surface reconstruction for aerial manipulation

Real-time dense surface reconstruction for aerial manipulati...

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IEEE/RSJ International Conference on Intelligent Robots and systems

作者： Marco Karrer Mina Kamel Roland Siegwart Margarita Chli Vision for Robotics Lab ETH Zurich Switzerland Autonomous Systems Lab ETH Zurich Switzerland

ISBN: (纸本)9781509037636

With robotic systems reaching considerable maturity in basic self-localization and environment mapping, new research avenues open up pushing for interaction of a robot with its surroundings for added autonomy. However, the transition from traditionally sparse feature-based maps to dense and accurate scene-estimation imperative for realistic manipulation is not straightforward. Moreover, achieving this level of scene perception in real-time from a computationally constrained and highly shaky and agile platform, such as a small an Unmanned Aerial Vehicle (UAV) is perhaps the most challenging scenario for perception for manipulation. Drawing inspiration from otherwise computationally constraining Computer Vision techniques, we present a system combining visual, inertial and depth information to achieve dense, local scene reconstruction of high precision in real-time. Our evaluation testbed is formed using ground-truth not only in the pose of the sensor-suite, but also the scene reconstruction using a highly accurate laser scanner, offering unprecedented comparisons of scene estimation to ground-truth using real sensor data. Given the lack of any real, ground-truth datasets for environment reconstruction, our V4RL Dense Surface Reconstruction dataset is publicly available.

关键词： Cameras Calibration Real-time systems Simultaneous localization and mapping Unmanned aerial vehicles simultaneous localization and mapping real-time systems Cameras drone honey bees Unmanned Aerial Vehicle Surface reconstruction ground truth Dense vehicle Sensor Data

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Bayesian on-line learning of driving behaviors

Bayesian on-line learning of driving behaviors

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IEEE International Conference on robotics and Automation (ICRA)

作者： Jérôme Maye Rudolph Triebel Luciano Spinello Roland Siegwart Autonomous Systems Lab ETH Zurich Switzerland Social Robotics Lab University of Freiburg Germany

This paper presents a novel self-supervised online learning method to discover driving behaviors from data acquired with an inertial measurement unit (IMU) and a camera. Both sensors where mounted in a car that was driven by a human through a typical city environment with intersections, pedestrian crossings and traffic lights. The presented system extracts motion segments from the IMU data and relates them to visual cues obtained from camera data. It employs a Bayesian on-line estimation method to discover the motion segments based on change-point detection and uses a Dirichlet Compound Multinomial (DCM) model to represent the visual features extracted from the camera images. By incorporating these visual cues into the on-line estimation process, labels are computed that are equal for similar motion segments. As a result, typical traffic situations such as braking maneuvers in front of a red light can be identified automatically. Furthermore, appropriate actions in form of observed motion changes are associated to the discovered traffic situations. The approach is evaluated on a real data set acquired in the center of Zurich.

关键词： Motion segmentation Computational modeling Vehicles Visualization Cameras Image segmentation Data models

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