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Computationally efficient dense moving object detection based on reduced space disparity estimation

IFAC-PapersOnLine 2018年第22期51卷 360-365页

作者： Popovi, Goran Hadviger, Antea Markovi, Ivan Petrovi, Ivan University of Zagreb Faculty of Electrical Engineering and Computing Laboratory for Autonomous Systems and Mobile Robotics Zagreb Croatia

Computationally efficient moving object detection and depth estimation from a stereo camera is an extremely useful tool for many computer vision applications, including robotics and autonomous driving. In this paper we show how moving objects can be densely detected by estimating disparity using an algorithm that improves complexity and accuracy of stereo matching by relying on information from previous frames. The main idea behind this approach is that by using the ego-motion estimation and the disparity map of the previous frame, we can set a prior base that enables us to reduce the complexity of the current frame disparity estimation, subsequently also detecting moving objects in the scene. For each pixel we run a Kalman filter that recursively fuses the disparity prediction and reduced space semi-global matching (SGM) measurements. The proposed algorithm has been implemented and optimized using streaming single instruction multiple data instruction set and multi-threading. Furthermore, in order to estimate the process and measurement noise as reliably as possible, we conduct extensive experiments on the KITTI suite using the ground truth obtained by the 3D laser range sensor. Concerning disparity estimation, compared to the OpenCV SGM implementation, the proposed method yields improvement on the KITTI dataset sequences in terms of both speed and accuracy. © 2018

关键词： Computer vision Computational complexity Computational efficiency Motion estimation Object detection Object recognition Stereo image processing Stereo vision 3D laser range sensors Computationally efficient Computer vision applications Dense disparity estimation Detecting moving objects Moving object detection Semi global matching Single instruction multiple data instructions

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Fault-Tolerant Control of a Hexacopter UAV based on Self-Scheduled Control Allocation

Fault-Tolerant Control of a Hexacopter UAV based on Self-Sch...

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2018 International Conference on Unmanned Aircraft systems, ICUAS 2018

作者： Nguyen, Duc-Tien Saussie, David Saydy, Lahcen Polytechnique Montreal Mobile Robotics and Autonomous Systems Laboratory Canada

ISBN: (纸本)9781538613535

In this paper, a control allocation (CA) algorithm for the fault-tolerant control (FTC) system of a multirotor unmanned aerial vehicle (UAV) is presented. The proposed CA design is based on gain-scheduling control in the framework of structured H synthesis. Given the appropriate fault detection and diagnosis (FDD) system, allocation laws are parameterized as polynomial functions of actuators effectiveness levels. The polynomial coefficients are then tuned with Matlab-based function systune to meet the robustness and fault tolerance requirements. Unlike conventional CA schemes in the literature, the proposed method has the advantage of not solving any online optimization problem. Moreover, actuator constraints after a failure are also taken into account during the tuning process using a multi-model approach. In comparison with other FTC systems based on reconfigurable controller, this smooth self-scheduled CA has a very simple structure and allows one to avoid undesirable transient phenomena during the controller reconfiguration process. High fidelity simulations and experimental results performed on a hexacopter show the effectiveness and robustness of the proposed FTC in accommodating different levels of actuator degradation. © 2018 IEEE.

关键词： Actuators

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Stereo event lifetime and disparity estimation for dynamic vision sensors

arXiv

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arXiv 2019年

作者： Hadviger, Antea Marković, Ivan Petrović, Ivan University of Zagreb Faculty of Electrical Engineering and Computing Laboratory for Autonomous Systems and Mobile Robotics Croatia

Event-based cameras are biologically inspired sensors that output asynchronous pixel-wise brightness changes in the scene called events. They have a high dynamic range and temporal resolution of a microsecond, opposed to standard cameras that output frames at fixed frame rates and suffer from motion blur. Forming stereo pairs of such cameras can open novel application possibilities, since for each event depth can be readily estimated;however, to fully exploit asynchronous nature of the sensor and avoid fixed time interval event accumulation, stereo event lifetime estimation should be employed. In this paper, we propose a novel method for event lifetime estimation of stereo event-cameras, allowing generation of sharp gradient images of events that serve as input to disparity estimation methods. Since a single brightness change triggers events in both event-camera sensors, we propose a method for single shot event lifetime and disparity estimation, with association via stereo matching. The proposed method is approximately twice as fast and more accurate than if lifetimes were estimated separately for each sensor and then stereo matched. Results are validated on realworld data through multiple stereo event-camera experiments. Copyright © 2019, The Authors. All rights reserved.

关键词： Cameras

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Stochastic optimization for trajectory planning with heteroscedastic Gaussian processes

arXiv

引用

arXiv 2019年

作者： Petrović, Luka Peršić, Juraj Seder, Marija Marković, Ivan University of Zagreb Faculty of Electrical Engineering and Computing Laboratory for Autonomous Systems and Mobile Robotics Croatia

Trajectory optimization methods for motion planning attempt to generate trajectories that minimize a suitable objective function. Such methods efficiently find solutions even for high degree-of-freedom robots. However, a globally optimal solution is often intractable in practice and state-of-the-art trajectory optimization methods are thus prone to local minima, especially in cluttered environments. In this paper, we propose a novel motion planning algorithm that employs stochastic optimization based on the cross-entropy method in order to tackle the local minima problem. We represent trajectories as samples from a continuous-time Gaussian process and introduce heteroscedasticity to generate powerful trajectory priors better suited for collision avoidance in motion planning problems. Our experimental evaluation shows that the proposed approach yields a more thorough exploration of the solution space and a higher success rate in complex environments than a current Gaussian process based state-of-the-art trajectory optimization method, namely GPMP2, while having comparable execution time. Copyright © 2019, The Authors. All rights reserved.

关键词： Gaussian distribution

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Spatio-temporal multisensor calibration based on gaussian processes moving object tracking

arXiv

引用

arXiv 2019年

作者： Peršić, Juraj Petrović, Luka Marković, Ivan Petrović, Ivan University of Zagreb Faculty of Electrical Engineering and Computing Laboratory for Autonomous Systems and Mobile Robotics Croatia

Perception is one of the key abilities of autonomous mobile robotic systems, which often relies on fusion of heterogeneous sensors. Although this heterogeneity presents a challenge for sensor calibration, it is also the main prospect for reliability and robustness of autonomous systems. In this paper, we propose a method for multisensor calibration based on Gaussian processes (GPs) estimated moving object trajectories, resulting with temporal and extrinsic parameters. The appealing properties of the proposed temporal calibration method are: Coordinate frame invariance, thus avoiding prior extrinsic calibration, theoretically grounded batch state estimation and interpolation using GPs, computational efficiency with O(n) complexity, leveraging data already available in autonomous robot platforms, and the end result enabling 3D point-to-point extrinsic multisensor calibration. The proposed method is validated both in simulations and real-world experiments. For real-world experiment we evaluated the method on two multisensor systems: An externally triggered stereo camera, thus having temporal ground truth readily available, and a heterogeneous combination of a camera and motion capture system. The results show that the estimated time delays are accurate up to a fraction of the fastest sensor sampling time. Copyright © 2019, The Authors. All rights reserved.

关键词： Gaussian distribution

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Stochastic Optimization for Trajectory Planning with Heteroscedastic Gaussian Processes

Stochastic Optimization for Trajectory Planning with Heteros...

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European Conference on mobile Robots (ECMR)

作者： Luka Petrović Juraj Peršić Marija Seder Ivan Marković University of Zagreb Faculty of Electrical Engineering and Computing Laboratory for Autonomous Systems and Mobile Robotics Croatia

关键词： Planning Trajectory optimization Robots Collision avoidance Gaussian processes

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Human intention recognition for human aware planning in integrated warehouse systems

arXiv

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arXiv 2020年

作者： Petković, Tomislav Hvězda, Jakub Rybecký, Tomáš Marković, Ivan Kulich, Miroslav Přeučil, Libor Petrović, Ivan University of Zagreb Faculty of Electrical Engineering and Computing Department of Control and Computer Engineering Laboratory for Autonomous Systems and Mobile Robotics Unska 3 ZagrebHR-10000 Croatia Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Prague160 00 Czech Republic

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. Copyright © 2020, The Authors. All rights reserved.

关键词： mobile robots

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Multi-agent Gaussian Process Motion Planning via Probabilistic Inference

Multi-agent Gaussian Process Motion Planning via Probabilist...

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作者： Petrović, Luka Marković, 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： Trajectories

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Fast Manipulability Maximization Using Continuous-Time Trajectory optimization

Fast Manipulability Maximization Using Continuous-Time Traje...

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2019 IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Filip Marić Oliver Limoyo Luka Petrović Trevor Ablett Ivan Petrović Jonathan Kelly Space and Terrestrial Autonomous Robotic Systems Laboratory University of Toronto Institute for Aerospace Studies Canada Laboratory for Autonomous Systems and Mobile Robotics University of Zagreb Faculty of Elecmcal Engineenng and Computing Croatia

A significant challenge in manipulation motion planning is to ensure agility in the face of unpredictable changes during task execution. This requires the identification and possible modification of suitable joint-space trajectories, since the joint velocities required to achieve a specific endeffector motion vary with manipulator configuration. For a given manipulator configuration, the joint space-to-task space velocity mapping is characterized by a quantity known as the manipulability index. In contrast to previous control-based approaches, we examine the maximization of manipulability during planning as a way of achieving adaptable and safe joint space-to-task space motion mappings in various scenarios. By representing the manipulator trajectory as a continuous-time Gaussian process (GP), we are able to leverage recent advances in trajectory optimization to maximize the manipulability index during trajectory generation. Moreover, the sparsity of our chosen representation reduces the typically large computational cost associated with maximizing manipulability when additional constraints exist. Results from simulation studies and experiments with a real manipulator demonstrate increases in manipulability, while maintaining smooth trajectories with more dexterous (and therefore more agile) arm configurations.

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Fast manipulability maximization using continuous-time trajectory optimization

arXiv

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arXiv 2019年

作者： Marić, Filip Limoyo, Oliver Petrović, Luka Ablett, Trevor Petrović, Ivan Kelly, Jonathan 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

关键词： Continuous time systems

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