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Long-Term Topological Localisation for Service Robots in Dynamic Environments using Spectral Maps

Long-Term Topological Localisation for Service Robots in Dyn...

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IEEE/RSJ International Conference on Intelligent Robots and systems

作者： Tomas Krajnik Jaime P. Fentanes Oscar M. Mozos Tom Duckett Johan Ekekrantz Marc Hanheide Lincoln Centre for Autonomous Systems University of Lincoln United Kingdom Computer Vision and Active Perception Lab The Royal Institute of Technology (KTH) Sweden

ISBN: (纸本)9781479969357

This paper presents a new approach for topological localisation of service robots in dynamic indoor environments. In contrast to typical localisation approaches that rely mainly on static parts of the environment, our approach makes explicit use of information about changes by learning and modelling the spatio-temporal dynamics of the environment where the robot is acting. The proposed spatio-temporal world model is able to predict environmental changes in time, allowing the robot to improve its localisation capabilities during long-term operations in populated environments. To investigate the proposed approach, we have enabled a mobile robot to autonomously patrol a populated environment over a period of one week while building the proposed model representation. We demonstrate that the experience learned during one week is applicable for topological localization even after a hiatus of three months by showing that the localization error rate is significantly lower compared to static environment representations.

关键词： Service robots Environments Robot indoor environment Mobile robots Environmental changes Explicit

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Object and pose recognition using contour and shape information

Object and pose recognition using contour and shape informat...

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International Conference on Advanced Robotics (ICAR)

作者： H. Cornelius D. Kragic J.-O. Eklundh Computational Vision and Active Perception Laboratory Centre for Autonomous Systems Department of Numerical Analysis and Computing Science Royal Institute of Technology Stockholm Sweden

Object recognition and pose estimation are of significant importance for robotic visual servoing, manipulation and grasping tasks. Traditionally, contour and shape based methods have been considered as most adequate for estimating stable and feasible grasps (Bicchi and Kumar, 2000). A new research direction has been advocated in visual servoing where image moments are used to define a suitable error function to be minimized. Compared to appearance based methods, contour and shape based approaches are also suitable for use with range sensors such as, for example, lasers. In this paper, we evaluate a contour based object recognition system building on the method in Nelson and Selinger (1998), suitable for objects of uniform color properties such as cups, cutlery, fruits etc. This system is one of the building blocks of a more complex object recognition system based both on stereo and appearance cues, (Bjorkman and Kragic, 2004). The system has a significant potential both in terms of service robot and programming by demonstration tasks. Experimental evaluation shows promising results in terms of robustness to occlusion and noise

关键词： Shape Object recognition Service robots Robot programming Visual servoing Robotics and automation computer vision Noise robustness Manufacturing industries Automatic programming

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Robot navigation under uncertainties using event based sampling

Robot navigation under uncertainties using event based sampl...

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IEEE Conference on Decision and Control

作者： Michele Colledanchise Dimos V. Dimarogonas Petter Ögren Centre for Autonomous Systems The Royal Institute of Technology - KTH Stockholm Sweden Centre for Autonomous Systems Computer Vision and Active Perception Lab School of Computer Science and Communication The Royal Institute of Technology - KTH Stockholm Sweden

In many robot applications, sensor feedback is needed to reduce uncertainties in environment models. However, sensor data acquisition also induces costs in terms of the time elapsed to make the observations and the computations needed to find new estimates. In this paper, we show how to use event based sampling to reduce the number of measurements done, thereby saving time, computational resources and power, without jeopardizing critical system properties such as safety and goal convergence. This is done by combining recent advances in nonlinear estimation with event based control using artificial potential fields. The results are particularly useful for real time systems such as high speed vehicles or teleoperated robots, where the cost of taking measurements is even higher, in terms of stops or transmission times. We conclude the paper with a set of simulations to illustrate the effectiveness of the approach and compare it with a baseline approach using periodic measurements.

关键词： Robot sensing systems Collision avoidance Uncertainty Time measurement Bismuth Navigation

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Predicting slippage and learning manipulation affordances through Gaussian Process regression

Predicting slippage and learning manipulation affordances th...

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IEEE-RAS International Conference on Humanoid Robots

作者： Francisco E. Viña B. Yasemin Bekiroglu Christian Smith Yiannis Karayiannidis Danica Kragic Computer Vision and Active Perception Lab. Centre for Autonomous Systems School of Computer Science and Communication Royal Institute of Technology KTH SE-100 44 Stockholm Sweden Computer Vision and Active Perception Lab Royal Institute of Technology KTH Stockholm Sweden

Object grasping is commonly followed by some form of object manipulation - either when using the grasped object as a tool or actively changing its position in the hand through in-hand manipulation to afford further interaction. In this process, slippage may occur due to inappropriate contact forces, various types of noise and/or due to the unexpected interaction or collision with the environment. In this paper, we study the problem of identifying continuous bounds on the forces and torques that can be applied on a grasped object before slippage occurs. We model the problem as kinesthetic rather than cutaneous learning given that the measurements originate from a wrist mounted force-torque sensor. Given the continuous output, this regression problem is solved using a Gaussian Process approach. We demonstrate a dual armed humanoid robot that can autonomously learn force and torque bounds and use these to execute actions on objects such as sliding and pushing. We show that the model can be used not only for the detection of maximum allowable forces and torques but also for potentially identifying what types of tasks, denoted as manipulation affordances, a specific grasp configuration allows. The latter can then be used to either avoid specific motions or as a simple step of achieving in-hand manipulation of objects through interaction with the environment.

关键词： Friction Robot sensing systems Force Torque Grasping

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Grasp recognition and mapping on humanoid robots

Grasp recognition and mapping on humanoid robots

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IEEE-RAS International Conference on Humanoid Robots

作者： Martin Do Javier Romero Hedvig Kjellström Pedram Azad Tamim Asfour Danica Kragic Rüdiger Dillmann Institute for Anthropomatics University of Karlsruhe Karlsruhe Germany Royal Institute of Technology Computer Vision & Active Perception Laboratory Centre for Autonomous Systems KTH Royal Institute of Technology Stockholm Sweden

ISBN: (纸本)9781424445875

In this paper, we present a system for vision-based grasp recognition, mapping and execution on a humanoid robot to provide an intuitive and natural communication channel between humans and humanoids. This channel enables a human user to teach a robot how to grasp an object. The system comprises three components: human upper body motion capture system which provides the approaching direction towards an object, hand pose estimation and grasp recognition system, which provides the grasp type performed by the human as well as a grasp mapping and execution system for grasp reproduction on a humanoid robot with five-fingered hands. All three components are real-time and markerless. Once an object is reached, the hand posture is estimated, including hand orientation and grasp type. For the execution on a robot, hand posture and approach movement are mapped and optimized according to the kinematic limitations of the robot. Experimental results are performed on the humanoid robot ARMAR-IIIb.

关键词： Humanoid robots Humans Educational robots Robot vision systems Communication channels Motion estimation Kinematics Robot programming Education Anthropomorphism

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Object recognition using saliency maps and HTM learning

Object recognition using saliency maps and HTM learning

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IEEE International Workshop on Imaging systems and Techniques (IST)

作者： Ioannis Kostavelis Lazaros Nalpantidis Antonios Gasteratos Laboratory of Robotics and Automation Department of Production and Management Engineering Democritus University of Thrace Xanthi Greece Computer Vision and Active Perception Laboratory Centre for Autonomous Systems Royal Institute of Technology Stockholm Sweden

In this paper a pattern classification and object recognition approach based on bio-inspired techniques is presented. It exploits the Hierarchical Temporal Memory (HTM) topology, which imitates human neocortex for recognition and categorization tasks. The HTM comprises a hierarchical tree structure that exploits enhanced spatiotemporal modules to memorize objects appearing in various orientations. In accordance with HTM's biological inspiration, human vision mechanisms can be used to preprocess the input images. Therefore, the input images undergo a saliency computation step, revealing the plausible information of the scene, where a human might fixate. The adoption of the saliency detection module releases the HTM network from memorizing redundant information and augments the classification accuracy. The efficiency of the proposed framework has been experimentally evaluated in the ETH-80 dataset, and the classification accuracy has been found to be greater than other HTM systems.

关键词： Correlation Quantization Vectors Accuracy Support vector machines Object recognition Humans

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Saliency ranking for benthic survey using underwater images

Saliency ranking for benthic survey using underwater images

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International Conference on Control, Automation, Robotics and vision (ICARCV)

作者： Matthew Johnson-Roberson Oscar Pizarro Stefan Williams Computational Vision and Active Perception Laboratory School of Computer Science and Communication Royal Institute of Technology Sweden ARC Centre of Excellence of Autonomous Systems Australian Centre of Field Robotics University of Sydney Australia

This paper presents a novel architecture for a classification system based on the visual saliency of images. The work is motivated by the difficulty of reviewing large numbers of images as a human operator in the context of autonomous Underwater Vehicle (AUV) surveys. We formulate a feature space in which an algorithm operates over color and texture to determine saliency and illustrate how this can be used to find interesting or unusual images within a large data set. The saliency classification based on these general image features allows for overlays highlighting interesting benthos or geologic structures on large scale 3D seafloor reconstructions, quickly providing spatial context to human observers. These results are validated using a set of human trials in which images are classified into salient and non-salient categories by a number of test subjects. The trials show good agreement both between subjects and between the human labels and the automated classification system. The results of the automated technique are also compared directly to a more traditional SVM classification system showing favorable results for our system for generalizing to new environments.

关键词： Pixel Humans Visualization Entropy Image color analysis Indexes Context

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Folding Assembly by Means of Dual-Arm Robotic Manipulation

Folding Assembly by Means of Dual-Arm Robotic Manipulation

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IEEE International Conference on Robotics and Automation

作者： Diogo Almeida Yiannis Karayiannidis Computer Vision and Active Perception Lab. Centre for Autonomous Systems School of Computer Science and Communication Royal Institute of Technology KTH SE-100 44 Stockholm Sweden

ISBN: (纸本)9781467380270

In this paper, we consider folding assembly as an assembly primitive suitable for dual-arm robotic assembly, that can be integrated in a higher level assembly strategy. The system composed by two pieces in contact is modelled as an articulated object, connected by a prismatic-revolute joint. Different grasping scenarios were considered in order to model the system, and a simple controller based on feedback linearisation is proposed, using force torque measurements to compute the contact point kinematics. The folding assembly controller has been experimentally tested with two sample parts, in order to showcase folding assembly as a viable assembly primitive.

关键词： Means Dual-Arm Robotic Manipulation system

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Action Recognition and Understanding using Motor Primitives

Action Recognition and Understanding using Motor Primitives

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Ville Kyrki Isabel Serrano Vicente Danica Kragic Jan-Olof Eklundh Department of Information Technology Lappeenranta University of Technology Finland Computational Vision and Active Perception Laboratory Centre for Autonomous Systems Royal Institute of Technology Stockholm Sweden

We investigate modeling and recognition of arm manipulation actions of different levels of complexity. To model the process, we are using a combination of discriminative support vector machines and generative hidden Markov models. The experimental evaluation, performed with 10 people, investigates both definition and structure of primitive motions as well as the validity of the modeling approach taken.

关键词： Hidden Markov models Support vector machines Support vector machine classification Vocabulary Human robot interaction Robot vision systems Information technology computer vision Performance evaluation Mirrors

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Generalizing grasps across partly similar objects

Generalizing grasps across partly similar objects

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Renaud Detry Carl Henrik Ek Marianna Madry Justus Piater Danica Kragic Centre for Autonomous Systems and the Computer Vision and Active Perception Lab CSC KTH Royal Institute of Technology Stockholm Sweden Intelligent and Interactive Systems group Institute of Computer Science University of Innsbruck Austria

The paper starts by reviewing the challenges associated to grasp planning, and previous work on robot grasping. Our review emphasizes the importance of agents that generalize grasping strategies across objects, and that are able to transfer these strategies to novel objects. In the rest of the paper, we then devise a novel approach to the grasp transfer problem, where generalization is achieved by learning, from a set of grasp examples, a dictionary of object parts by which objects are often grasped. We detail the application of dimensionality reduction and unsupervised clustering algorithms to the end of identifying the size and shape of parts that often predict the application of a grasp. The learned dictionary allows our agent to grasp novel objects which share a part with previously seen objects, by matching the learned parts to the current view of the new object, and selecting the grasp associated to the best-fitting part. We present and discuss a proof-of-concept experiment in which a dictionary is learned from a set of synthetic grasp examples. While prior work in this area focused primarily on shape analysis (parts identified, e.g., through visual clustering, or salient structure analysis), the key aspect of this work is the emergence of parts from both object shape and grasp examples. As a result, parts intrinsically encode the intention of executing a grasp.

关键词： Grasping Shape Planning Dictionaries Databases Grippers Covariance matrix

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