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arXiv 2021年

作者： Hadviger, Antea Cvišić, Igor Marković, Ivan Vražić, Sacha Petrović, Ivan University of Zagreb Faculty of Electrical Engineering and Computing Laboratory for Autonomous Systems and Mobile Robotics Croatia Rimac Automobili Croatia

Event-based cameras are biologically inspired sensors that output events, i.e., asynchronous pixel-wise brightness changes in the scene. Their high dynamic range and temporal resolution of a microsecond makes them more reliable than standard cameras in environments of challenging illumination and in high-speed scenarios, thus developing odometry algorithms based solely on event cameras offers exciting new possibilities for autonomous systems and robots. In this paper, we propose a novel stereo visual odometry method for event cameras based on feature detection and matching with careful feature management, while pose estimation is done by reprojection error minimization. We evaluate the performance of the proposed method on two publicly available datasets: MVSEC sequences captured by an indoor flying drone and DSEC outdoor driving sequences. MVSEC offers accurate ground truth from motion capture, while for DSEC, which does not offer ground truth, in order to obtain a reference trajectory on the standard camera frames we used our SOFT visual odometry, one of the highest ranking algorithms on the KITTI scoreboards. We compared our method to the ESVO method, which is the first and still the only stereo event odometry method, showing on par performance on the MVSEC sequences, while on the DSEC dataset ESVO, unlike our method, was unable to handle outdoor driving scenario with default parameters. Furthermore, two important advantages of our method over ESVO are that it adapts tracking frequency to the asynchronous event rate and does not require initialization. © 2021, CC BY.

关键词： Cameras

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Auto-Augmentation Contrastive Learning for Wearable-based Human Activity Recognition

Auto-Augmentation Contrastive Learning for Wearable-based Hu...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Qingyu Wu Jianfei Shen Feiyi Fan Yang Gu Chenyang Xu Yiqiang Chen Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China The Beijing Key Laboratory of Mobile Computing and Pervasive Device Beijing China Shandong Academy of Intelligent Computing Technology ShanDong China Peng Cheng Laboratory Shenzhen China Institute of Robotics and Autonomous Systems Tianjin University Tianjin China

For low-semantic sensor signals from human activity recognition (HAR), contrastive learning (CL) is essential to implement novel applications or generic models without manual annotation, which is a high-performance self-supervised learning (SSL) method. However, CL relies heavily on data augmentation for pairwise comparisons. Especially for low semantic data in the HAR area, conducting good performance augmentation strategies in pretext tasks still rely on manual attempts lacking generalizability and flexibility. To reduce the augmentation burden, we propose an end-to-end auto-augmentation contrastive learning (AutoCL) method for wearable-based HAR. AutoCL is based on a Siamese network architecture that shares the parameters of the backbone and with a generator embedded to learn auto-augmentation. AutoCL trains the generator based on the representation in the latent space to overcome the disturbances caused by noise and redundant information in raw sensor data. The architecture empirical study indicates the effectiveness of this design. Furthermore, we propose a stop-gradient design and correlation reduction strategy in AutoCL to enhance encoder representation learning. Extensive experiments based on four wide-used HAR datasets demonstrate that the proposed AutoCL method significantly improves recognition accuracy compared with other SOTA methods.

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Riemannian Optimization for Distance-Geometric Inverse Kinematics

arXiv

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arXiv 2021年

作者： Marić, Filip Giamou, Matthew Hall, Adam W. Khoubyarian, Soroush Petrović, Ivan Kelly, Jonathan Space and Terrestrial Autonomous Robotic Systems Laboratory University of Toronto Institute for Aerospace Studies Toronto Canada Laboratory for Autonomous Systems and Mobile Robotics University of Zagreb Faculty of Electrical Engineering and Computing Zagreb Croatia Dynamic Systems Laboratory University of Toronto Institute for Aerospace Studies Toronto Canada

Solving the inverse kinematics problem is a fundamental challenge in motion planning, control, and calibration for articulated robots. Kinematic models for these robots are typically parametrized by joint angles, generating a complicated mapping between the robot configuration and the end-effector pose. Alternatively, the kinematic model and task constraints can be represented using invariant distances between points attached to the robot. In this paper, we formalize the equivalence of distance-based inverse kinematics and the distance geometry problem for a large class of articulated robots and task constraints. Unlike previous approaches, we use the connection between distance geometry and low-rank matrix completion to find inverse kinematics solutions by completing a partial Euclidean distance matrix through local optimization. Furthermore, we parametrize the space of Euclidean distance matrices with the Riemannian manifold of fixed-rank Gram matrices, allowing us to leverage a variety of mature Riemannian optimization methods. Finally, we show that bound smoothing can be used to generate informed initializations without significant computational overhead, improving convergence. We demonstrate that our inverse kinematics solver achieves higher success rates than traditional techniques, and substantially outperforms them on problems that involve many workspace constraints. Copyright © 2021, The Authors. All rights reserved.

关键词： Motion planning

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A continuous-time approach for 3D radar-to-camera extrinsic calibration

arXiv

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arXiv 2021年

作者： Wise, Emmett Peršić, Juraj Grebe, Christopher Petrović, Ivan Kelly, Jonathan Laboratory The University of Toronto Institute for Aerospace Studies Toronto Canada The Laboratory for Autonomous Systems and Mobile Robotics University of Zagreb Faculty of Electrical Engineering and Computing Croatia Croatia Vector Institute Faculty Affiliate

Reliable operation in inclement weather is essential to the deployment of safe autonomous vehicles (AVs). Robustness and reliability can be achieved by fusing data from the standard AV sensor suite (i.e., lidars, cameras) with weather robust sensors, such as millimetre-wavelength radar. Critically, accurate sensor data fusion requires knowledge of the rigid-body transform between sensor pairs, which can be determined through the process of extrinsic calibration. A number of extrinsic calibration algorithms have been designed for 2D (planar) radar sensors-however, recently-developed, low-cost 3D millimetre-wavelength radars are set to displace their 2D counterparts in many applications. In this paper, we present a continuous-time 3D radar-to-camera extrinsic calibration algorithm that utilizes radar velocity measurements and, unlike the majority of existing techniques, does not require specialized radar retroreflectors to be present in the environment. We derive the observability properties of our formulation and demonstrate the efficacy of our algorithm through synthetic and real-world experiments. Copyright © 2021, The Authors. All rights reserved.

关键词： Cameras

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Gaussian Processes Incremental Inference for mobile Robots Dynamic Planning ⁎

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IFAC-PapersOnLine 2020年第2期53卷 9584-9589页

作者： Luka Petrović Filip Marić Ivan Marković Ivan Petrović University of Zagreb Faculty of Electrical Engineering and Computing Laboratory for Autonomous Systems and Mobile Robotics Croatia University of Toronto Institute for Aerospace Studies Space & Terrestrial Autonomous Robotic Systems Laboratory Canada

Trajectory optimization methods for motion planning attempt to generate trajectories that minimize a suitable objective function. While such methods efficiently find solutions in static environments, they need to be ran from scratch multiple times in the presence of moving obstacles, which incurs unnecessary computation and slows down execution. In this paper, we propose a trajectory optimization algorithm that anticipates the movement of obstacles and solves the planning problem in an iterative manner. We employ continuous-time Gaussian processes as trajectory representations both for the mobile robot and moving obstacles for which future locations are predicted according to a given model. We formulate the simultaneous moving obstacles tracking and mobile robot motion planning problem as probabilistic inference on a factor graph. Since trajectories of moving obstacles are optimized concurrently to motion planning, the proposed approach works in a predictive manner. After computing the initial solution, we use incremental inference for online replanning after an estimate of the moving obstacle position is provided. Our experimental evaluation demonstrates that the proposed approach supports online motion generation in the presence of moving obstacles.

关键词： motion planning trajectory optimization gaussian processes factor graphs incremental inference

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Online 3D Frontier-Based UGV and UAV Exploration Using Direct Point Cloud Visibility

Online 3D Frontier-Based UGV and UAV Exploration Using Direc...

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International Conference on Multisensor Fusion and Integration for Intelligent systems (MFI)

作者： Jason Williams Shu Jiang Matthew O’Brien Glenn Wagner Emili Hernandez Mark Cox Alex Pitt Ron Arkin Nicolas Hudson CSIRO Data61 Robotics and Autonomous Systems Group Georgia Tech Mobile Robot Laboratory Emesent

ISBN: (数字)9781728164229

ISBN: (纸本)9781728164236

While robots have long been proposed as a tool to reduce human personnel's exposure to danger in subterranean environments, these environments also present significant challenges to the development of these robots. Fundamental to this challenge is the problem of autonomous exploration. Frontier-based methods have been a powerful and successful approach to exploration, but complex 3D environments remain a challenge when online employment is required. This paper presents a new approach that addresses the complexity of operating in 3D by directly modelling the boundary between observed free and unobserved space (the frontier), rather than utilising dense 3D volumetric representations. By avoiding a representation involving a single map, it also achieves scalability to problems where Simultaneous Localisation and Matching (SLAM) loop closures are essential. The approach enabled a team of seven ground and air robots to autonomously explore the DARPA Subterranean Challenge Urban Circuit, jointly traversing over 8 km in a complex and communication denied environment.

关键词： Exploration Robot Environments Three Dimensions drone honey bees Unmanned Aerial Vehicle subsurface environment Scalability underground Danger

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Inverse Kinematics for Serial Kinematic Chains via Sum of Squares Optimization

Inverse Kinematics for Serial Kinematic Chains via Sum of Sq...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Filip Marić Matthew Giamou Soroush Khoubyarian Ivan Petrović Jonathan Kelly University of Toronto Institute for Aerospace Studies Space and Terrestrial Autonomous Robotic Systems Laboratory Canada University of Zagreb Faculty of Electrical Engineering and Computing Laboratory for Autonomous Systems and Mobile Robotics Croatia

ISBN: (数字)9781728173955

ISBN: (纸本)9781728173962

Inverse kinematics is a fundamental challenge for articulated robots: fast and accurate algorithms are needed for translating task-related workspace constraints and goals into feasible joint configurations. In general, inverse kinematics for serial kinematic chains is a difficult nonlinear problem, for which closed form solutions cannot easily be obtained. Therefore, computationally efficient numerical methods that can be adapted to a general class of manipulators are of great importance. In this paper, we use convex optimization techniques to solve the inverse kinematics problem with joint limit constraints for highly redundant serial kinematic chains with spherical joints in two and three dimensions. This is accomplished through a novel formulation of inverse kinematics as a nearest point problem, and with a fast sum of squares solver that exploits the sparsity of kinematic constraints for serial manipulators. Our method has the advantages of post-hoc certification of global optimality and a runtime that scales polynomially with the number of degrees of freedom. Additionally, we prove that our convex relaxation leads to a globally optimal solution when certain conditions are met, and demonstrate empirically that these conditions are common and represent many practical instances. Finally, we provide an open source implementation of our algorithm.

关键词： Kinematics Optimization Conferences Automation Robots Task analysis

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State and parameter estimation of suspended load using quadrotor onboard sensors

State and parameter estimation of suspended load using quadr...

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International Conference on Unmanned Aircraft systems (ICUAS)

作者： Vicko Prkačin Ivana Palunko Ivan Petrović Laboratory for intelligent autonomous systems (LARIAT) Department of Electrical and Computer Engineering University of Dubrovnik Dubrovnik Croatia Laboratory for autonomous systems and mobile robotics (LAMOR) Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia

ISBN: (数字)9781728142784

ISBN: (纸本)9781728142791

In this paper, we address the problem of state and parameter estimation of a suspended load using quadrotor onboard sensors. Flying with a suspended load alters the quadrotor flight dynamics, sometimes to a large extent, making it a challenging and hazardous task. Monitoring the state of the suspended load is vital for safe flight operations while parameter estimation decouples the control design from specific parameter-dependent solutions. We take advantage of the fact that the forces and torques the suspended load exerts on the quadrotor can be detected in the aircraft IMU measurements as a low frequency harmonic. Thus, by combining the available measurements and system mass we are able to estimate the state of the suspended load. Since our approach stems from understanding the aircraft-load interaction, we start off by delineating the full system model of the quadrotor with a suspended load. To isolate the natural frequency of the suspended load and determine the length of suspension cable, we employ the Fast Fourier Transform (FFT). The proposed estimation algorithms are validated through extensive numerical simulations and experimentally.

关键词： Estimation Aircraft Load modeling Sensors Atmospheric modeling Parameter estimation Numerical models

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Multi-agent Gaussian Process Motion Planning via Probabilistic Inference

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IFAC-PapersOnLine 2018年第22期51卷 160-165页

作者： Petrovi, Luka Markovi, Ivan Seder, Marija University of Zagreb Faculty of Electrical Engineering and Computing Laboratory for Autonomous Systems and Mobile Robotics Croatia

This paper deals with motion planning for multiple agents by representing the problem as a simultaneous optimization of every agent's trajectory. Each trajectory is considered as a sample from a one-dimensional continuous-time Gaussian process (GP) generated by a linear time-varying stochastic differential equation driven by white noise. By formulating the planning problem as probabilistic inference on a factor graph, the structure of the pertaining GP can be exploited to find the solution efficiently using numerical optimization. In contrast to planning each agent's trajectory individually, where only the current poses of other agents are taken into account, we propose simultaneous planning of multiple trajectories that works in a predictive manner. It takes into account the information about each agent's whereabouts at every future time instant, since full trajectories of each agent are found jointly during a single optimization procedure. We compare the proposed method to an individual trajectory planning approach, demonstrating significant improvement in both success rate and computational efficiency. © 2018

关键词： Motion planning Computational efficiency Continuous time systems Differential equations Gaussian distribution Gaussian noise (electronic) Multi agent systems One dimensional Optimization Stochastic systems Trajectories White noise Factor graphs Numerical optimizations Optimization procedures Probabilistic inference Simultaneous optimization Stochastic differential equations Trajectory optimization Trajectory Planning

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Human Intention Recognition for Human Aware Planning in Integrated Warehouse systems

Human Intention Recognition for Human Aware Planning in Inte...

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Mediterranean Conference on Control and Automation (MED)

作者： Tomislav Petković Jakub Hvězda Tomáš Rybecký Ivan Marković Miroslav Kulich Libor Přeučil Ivan Petrović Faculty of Electrical Engineering and Computing University of Zagreb Laboratory for Autonomous Systems and Mobile Robotics Unska 3 Zagreb Croatia Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Prague Czech Republic

ISBN: (数字)9781728157429

ISBN: (纸本)9781728157436

With the substantial growth of logistics businesses the need for larger and more automated warehouses increases, thus giving rise to fully robotized shop-floors with mobile robots in charge of transporting and distributing goods. However, even in fully automatized warehouse systems the need for human intervention frequently arises, whether because of maintenance or because of fulfilling specific orders, thus bringing mobile robots and humans ever closer in an integrated warehouse environment. In order to ensure smooth and efficient operation of such a warehouse, paths of both robots and humans need to be carefully planned; however, due to the possibility of humans deviating from the assigned path, this becomes an even more challenging task. Given that, the supervising system should be able to recognize human intentions and its alternative paths in real-time. In this paper, we propose a framework for human deviation detection and intention recognition which outputs the most probable paths of the humans workers and the planner that acts accordingly by replanning for robots to move out of the human's path. Experimental results demonstrate that the proposed framework increases total number of deliveries, especially human deliveries, and reduces human-robot encounters.

关键词： Robot kinematics Frequency modulation Planning Task analysis Hidden Markov models Trajectory

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