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检索条件"机构=ISR - Institute of Systems and Robotics and the Department of Electrical and Computer Engineering"

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Multimodal Human Detection Using YOLO and Representation Learning for Robot Perception

Multimodal Human Detection Using YOLO and Representation Lea...

引用

Iberian robotics Conference (ROBOT)

作者： Kennedy O. S. Mota Diogo S. De Oliveira Luís Garrote Cristiano Premebida Department of Electrical and Computer Engineering University of Coimbra Institute of Systems and Robotics Coimbra Portugal

ISBN: (数字)9798350376364

ISBN: (纸本)9798350376371

This work concentrates on the problem of multisensor people detection using YOLO trained on four distinct modalities: depth and intensity LiDAR-maps, RGB, and ‘thermal’ images. RGB cameras, ubiquitous in this application domain, offer great resolution but struggle with adverse lighting conditions resulting in overexposed or underexposed images which then impact negatively on the performance of the algorithms. Thermal (long-wave infrared) cameras are more resilient against varying light conditions and provide complementary textural features, although with lower resolution when compared to RGB cameras. LiDAR sensors, while having a significantly low resolution, contribute to a physically interpretable mapping of the environment providing precise information regarding size/dimension and location of the objects. The main goal of this work is to tackle people detection using deep-models trained on single and multi-modality representations. To support the experimental part this work introduces a new multimodal dataset (called MID-3K). MID-3K allows the development of data fusion strategies by leveraging four modalities (obtained from three distinct exteroceptive sensors mounted on a mobile robot). Leveraging on a single-modality YOLO framework, we propose a multimodal representation learning approach to improve the baseline performance and to capture more relevant features across all input modalities. The evaluation of the proposed detection pipeline is conducted on the MID-3K dataset, where the reported results are grounded on state-of-the-art performance measures. The new dataset is available in a GitHub repository 1 1 MID-3K dataset: https://***/MID-3K/.

关键词： YOLO Representation learning Training Image resolution Laser radar Robot vision systems Thermal sensors Sensor fusion Cameras Software development management

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T-SLAM: Registering topological and geometric maps for robot localization in large environments

T-SLAM: Registering topological and geometric maps for robot...

引用

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

作者： F. Ferreira I. Amorim R. Rocha J. Dias Institute of Systems and Robotics Department of Electrical and Computer Engineering University of Coimbra Coimbra Portugal

This article reports on a map building method that integrates topological and geometric maps created independently using multiple sensors. The procedure is termed T-SLAM to emphasize the integration of Topological and local Geometric maps that are created using a SLAM algorithm. The topological and metric representations are created independently, being local metric maps associated with topological places and registered at the topological level. The T-SLAM approach is mathematically formulated and applied to the localization problem within the Intelligent Robotic Porter System (IRPS) project, which is aimed at deploying mobile robots in large environments (e.g. airports). Some preliminary experimental results demonstrate the validity of the proposed method.

关键词： Robots Hidden Markov models Robot sensing systems Sensors Robot kinematics Aerospace electronics Indexes

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Improving Grasping Performance of Underactuated Two Finger Robotic Hands Using Variable Stiffness

Improving Grasping Performance of Underactuated Two Finger R...

引用

Iberian robotics Conference (ROBOT)

作者： Dylan Denizon Sedat Dogru Lino Marques Department of Electrical and Computer Engineering Institute of Systems and Robotics University of Coimbra Coimbra Portugal

ISBN: (数字)9798350376364

ISBN: (纸本)9798350376371

In this work, we aim to improve the performance of an underactuated anthropomorphic tendon driven robotic gripper with two fingers by allowing the stiffness of the finger joints to change as a function of the object shape. An optimization problem requiring that the gripper apply a constant total contact force with a minimum actuation force was formulated using a Genetic Algorithm (GA) approach in order to obtain a set of designs with geometrical parameters such as position and size of various elements of the fingers as well as stiffness of the joints. The optimization process was also repeated for the fixed stiffness case to compare the two approaches, demonstrating that the addition of variable stiffness allowed to achieve the same grasp with less actuation force.

关键词： Torque Shape Friction Force Grasping Grippers Joining processes Robots Tendons Genetic algorithms

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Self calibration of step-by-step based climbing robots

Self calibration of step-by-step based climbing robots

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2009 IEEE/RSJ International Conference on Intelligent Robots and systems

作者： Mahmoud Tavakoli Lino Marques Aníbal T. Almeida Institute for Systems and Robotics Department of Electrical and Computer Engineering University of Coimbra Coimbra Portugal

Fine manipulation of large industrial manipulators faces many problems due to well known error sources. Most climbing robots can be seen as mobile manipulators whose base is also moving across the climbing structure, and consequently adds some additional positioning errors. 3DCLIMBER is a serial mechanism pole climbing robot, developed at isr-UC. The preliminary tests of the robot showed that it is particularly important to position the grippers precisely in the appropriate pose before grasping the structure. Otherwise the accumulating error will impair autonomous climbing process by forcing the operator to stop the operation after a couple of steps in order to calibrate the robot. This paper describes a self calibrating method proposed to measure and compensate these errors.

关键词： Calibration Climbing robots Manipulators Grippers Mobile robots Intelligent robots USA Councils Error compensation System testing Actuators

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Improved Semantic-based Human Interaction Understanding Using Context-based knowledge

Improved Semantic-based Human Interaction Understanding Usin...

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IEEE International Conference on systems, Man, and Cybernetics

作者： Kamrad Khoshhal Roudposhti Jorge Dias Institute of Systems and Robotics Department of Electrical and Computer Engineering University of Coimbra Portugal.

ISBN: (纸本)9781479906505

This paper proposes a descriptive approach for context-based human activity analysis through an hierarchical framework in a scene understanding application. Each human movement with respect to himself, others and scene, can arise different layers of human activities analysis, which usually investigated separately depend on the application. Human behaviour can not be analysed properly, since the all different layers of information were not considered. The effect of using the different layers of information to increase the accuracy of the analysis is presented in the study. The contributions are, using different information layers such as human body parts movement and human-object interaction, in 3D space, to improve human activity analysis, and proposing a probabilistic and descriptive model, based on a well-known human movement descriptor and Bayesian Network (BN) approach. Thus, based on the mentioned framework, the model is generalizable and flexible which are necessary for having such an applicable system. The capability of the proposed approach is presented in the experiment's section.

关键词： Scene understanding hierarchical framework human interaction analysis Bayesian approach human movement analysis descriptive model Human locomotion Bayes methods Human Activities activity analysis

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Direct Least Square Fitting of Paracatadioptric Line Images

Direct Least Square Fitting of Paracatadioptric Line Images

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IEEE computer Society Conference on computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Joao P. Barreto Helder Araujo Institute of Systems and Robotics Department of Electrical and Computer Engineering University of Coimbra Coimbra Portugal

Paracatadioptric sensors combine a parabolic shaped mirror and a camera inducing an orthographic projection. Such a configuration provides a wide field of view while keeping a single effective viewpoint. In general the paracatadioptric image of a line is a conic curve. The estimation of line images is an important subject for applications such as reconstruction and visual control of motion. However the estimation of the conic curves where lines are mapped is hard to accomplish. In general only a small arc of the conic is visible in the image and conventional conic fitting techniques are unable to correctly estimate the curve. This paper shows that line images can be accurately estimated by constraining the search space. A conic curve is the paracatadioptric image of a line if, and only if, the image of the circular points lie on the curve and two certain points are conjugate with respect to the conic. Considering the space of all conic curves, the line images lie in a linear subspace which depends on the system calibration. The paracatadioptric projection of a line can estimated by fitting a conic in the subspace to the data points. The proposed approach is computationally efficient since the fitting problem can be solved by an eigensystem

关键词： Fitting Equations Cameras Noise Least squares approximation Mirrors Sensors

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Exploiting 3D Grids for Indoor SLAM in Featureless Scenarios

Exploiting 3D Grids for Indoor SLAM in Featureless Scenarios

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IEEE International Conference on Autonomous Robot systems and Competitions (ICARSC)

作者： Luís Garrote Ulisses Reverendo Urbano J. Nunes Institute of Systems and Robotics Department of Electrical and Computer Engineering University of Coimbra Coimbra Portugal

ISBN: (数字)9798350352344

ISBN: (纸本)9798350352351

Accurate multi-sensor localization is a challenging task in the navigation of AMRs. Precise localization strategies are essential for AMRs to be able to perform with safety their missions in their surrounding environments. This work proposes a novel ROS-based modular 3D grid-based particle filter-based framework that can be used for Simultaneous Localization and Mapping (SLAM) or as a standalone robust localization strategy. The framework uses odometry and 3D LiDAR data as inputs for localization and SLAM. To further improve localization and representation alignment, a pose refinement stage is employed using Levenberg-Marquardt minimization. The refinement stage considers keypoints in the environment to improve localization and uses the raw 3D point cloud for map maintenance. A pyramid-like 3D grid resolution is used to aid the refinement of the representation, improving pose estimates in featureless scenarios. Experimental validation was carried out with data acquired using an in-house platform, in a set of indoor and semi-structured scenarios comprised of critical featureless areas. The obtained results highlight the robustness of the proposed framework in both SLAM and localization tasks. The code (ROS package) is made available in a GitHub repository 1 .

关键词： Location awareness Point cloud compression Three-dimensional displays Simultaneous localization and mapping Navigation Robustness Safety

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A Lidar and Vision-based Approach for Pedestrian and Vehicle Detection and Tracking

A Lidar and Vision-based Approach for Pedestrian and Vehicle...

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International Conference on Intelligent Transportation

作者： Cristiano Premebida Goncalo Monteiro Urbano Nunes Paulo Peixoto Institute of Systems and Robotics Department of Electrical and Computer Engineering University of Coimbra Coimbra Portugal

This paper presents a sensorial-cooperative architecture to detect, track and classify entities in semi-structured outdoor scenarios for intelligent vehicles. In order to accomplish this task, information provided by in-vehicle Lidar and monocular vision is used. The detection and tracking phases are performed in the laser space, and the object classification methods work both in laser space (using a Gaussian Mixture Model classifier) and in vision spaces (AdaBoost classifier). A Bayesian-sum decision rule is used in order to combine the results of both classification techniques, and hence a more reliable object classification is achieved. Experiments confirm the effectiveness of the proposed architecture.

关键词： Laser radar Vehicle detection Object detection Cameras Bayesian methods Intelligent transportation systems Intelligent vehicles Laser modes Machine vision Space technology

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3DCLIMBER: A climbing robot for inspection of 3D human made structures

3DCLIMBER: A climbing robot for inspection of 3D human made ...

引用

2008 IEEE/RSJ International Conference on Intelligent Robots and systems

作者： Mahmoud Tavakoli Ali Marjovi Lino Marques Anibal T. de Almeida Institute for Systems and Robotics Department of Electrical and Computer Engineering University of Coimbra Coimbra Portugal

ISBN: (纸本)9781424420575

3DCLIMBER is a running project in the University of Coimbra for developing a climbing robot with the capability of manipulating over 3D human-made structures. This paper mainly discuss the conceptual and detailed design and development of a Pole Climbing robot with minimum degrees of freedom which can climb over 3D structures with bends and branches followed by Preliminary test results of the robot performance. Electronics architecture and control algorithms are briefly described. The paper finishes with discussion of the current results and identifies some future works.

关键词： Robots Grippers Brushless motors DC motors Brushless DC motors AC motors Climbing robots

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Probabilistic LMA-based classification of human behaviour understanding using Power Spectrum technique

Probabilistic LMA-based classification of human behaviour un...

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International Conference on Information Fusion

作者： Kamrad Khoshhal Hadi Aliakbarpour João Quintas Paulo Drews Jorge Dias Institute of Systems and Robotics Department of Electrical and Computer Engineering University of Coimbra Coimbra Portugal

This paper proposes a new approach for the Power Spectrum (PS)-based feature extraction applied to probabilistic Laban Movement Analysis (LMA), for the sake of human behaviour understanding. A Bayesian network is presented to understand human action and behaviour based on 3D spatial data and using the LMA concept which is a known human movement descriptor. We have two steps for the classification process. The first step is estimating LMA parameters which are built to describe human motion situation by using some low level features. Then by having these parameters, it is possible to classify different human actions and behaviours. Here, a sample of using 3D acceleration data of six body parts to obtain some LMA parameters and understand some performed actions by human is shown. A new approach is applied to extract features from a signal data such as acceleration using the PS technique to achieve some of LMA parameters. A number of actions are defined, then a Bayesian network is used in learning and classification process. The experimental results prove that the proposed method is able to classify actions.

关键词： Humans Feature extraction Bayesian methods Frequency domain analysis Acceleration Three dimensional displays Legged locomotion

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