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Proceedings Article

ROS: an open-source Robot Operating System

01 Jan 2009-

TL;DR: This paper discusses how ROS relates to existing robot software frameworks, and briefly overview some of the available application software which uses ROS.

AbstractThis paper gives an overview of ROS, an opensource robot operating system. ROS is not an operating system in the traditional sense of process management and scheduling; rather, it provides a structured communications layer above the host operating systems of a heterogenous compute cluster. In this paper, we discuss how ROS relates to existing robot software frameworks, and briefly overview some of the available application software which uses ROS.

Topics: Robot software (55%), Open-source robotics (54%)

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Citations
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Journal ArticleDOI
TL;DR: It is shown that the optimal nearest neighbor algorithm and its parameters depend on the data set characteristics and an automated configuration procedure for finding the best algorithm to search a particular data set is described.
Abstract: For many computer vision and machine learning problems, large training sets are key for good performance. However, the most computationally expensive part of many computer vision and machine learning algorithms consists of finding nearest neighbor matches to high dimensional vectors that represent the training data. We propose new algorithms for approximate nearest neighbor matching and evaluate and compare them with previous algorithms. For matching high dimensional features, we find two algorithms to be the most efficient: the randomized k-d forest and a new algorithm proposed in this paper, the priority search k-means tree. We also propose a new algorithm for matching binary features by searching multiple hierarchical clustering trees and show it outperforms methods typically used in the literature. We show that the optimal nearest neighbor algorithm and its parameters depend on the data set characteristics and describe an automated configuration procedure for finding the best algorithm to search a particular data set. In order to scale to very large data sets that would otherwise not fit in the memory of a single machine, we propose a distributed nearest neighbor matching framework that can be used with any of the algorithms described in the paper. All this research has been released as an open source library called fast library for approximate nearest neighbors (FLANN), which has been incorporated into OpenCV and is now one of the most popular libraries for nearest neighbor matching.

1,183 citations


Cites background from "ROS: an open-source Robot Operating..."

  • ..., [60], [61], [62], [63], [64]) and is widely used in the computer vision community, in part due to its inclusion in OpenCV [65], the popular open source computer vision library....

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  • ...source projects, such as the point cloud library (PCL) and the robot operating system (ROS) [63]....

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Journal ArticleDOI
TL;DR: The open motion planning library is a new library for sampling-based motion planning, which contains implementations of many state-of-the-art planning algorithms, and it can be conveniently interfaced with other software components.
Abstract: The open motion planning library (OMPL) is a new library for sampling-based motion planning, which contains implementations of many state-of-the-art planning algorithms. The library is designed in a way that it allows the user to easily solve a variety of complex motion planning problems with minimal input. OMPL facilitates the addition of new motion planning algorithms, and it can be conveniently interfaced with other software components. A simple graphical user interface (GUI) built on top of the library, a number of tutorials, demos, and programming assignments are designed to teach students about sampling-based motion planning. The library is also available for use through Robot Operating System (ROS).

1,139 citations


Cites background from "ROS: an open-source Robot Operating..."

  • ...Section II gives some background on sampling-based motion planning and existing software packages for motion planning....

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Proceedings ArticleDOI
12 Jul 2014
TL;DR: The method achieves both low-drift and low-computational complexity without the need for high accuracy ranging or inertial measurements and can achieve accuracy at the level of state of the art offline batch methods.
Abstract: We propose a real-time method for odometry and mapping using range measurements from a 2-axis lidar moving in 6-DOF. The problem is hard because the range measurements are received at different times, and errors in motion estimation can cause mis-registration of the resulting point cloud. To date, coherent 3D maps can be built by off-line batch methods, often using loop closure to correct for drift over time. Our method achieves both low-drift and low-computational complexity without the need for high accuracy ranging or inertial measurements. The key idea in obtaining this level of performance is the division of the complex problem of simultaneous localization and mapping, which seeks to optimize a large number of variables simultaneously, by two algorithms. One algorithm performs odometry at a high frequency but low fidelity to estimate velocity of the lidar. Another algorithm runs at a frequency of an order of magnitude lower for fine matching and registration of the point cloud. Combination of the two algorithms allows the method to map in real-time. The method has been evaluated by a large set of experiments as well as on the KITTI odometry benchmark. The results indicate that the method can achieve accuracy at the level of state of the art offline batch methods.

1,077 citations


Proceedings ArticleDOI
01 Jan 2013
TL;DR: A versatile, scalable, yet powerful general-purpose robot simulation framework called V-REP, which allows for direct incorporation of various control techniques and renders simulations and simulation models more accessible to a general-public, by reducing the simulation model deployment complexity.
Abstract: From exploring planets to cleaning homes, the reach and versatility of robotics is vast. The integration of actuation, sensing and control makes robotics systems powerful, but complicates their simulation. This paper introduces a versatile, scalable, yet powerful general-purpose robot simulation framework called V-REP. The paper discusses the utility of a portable and flexible simulation framework that allows for direct incorporation of various control techniques. This renders simulations and simulation models more accessible to a general-public, by reducing the simulation model deployment complexity. It also increases productivity by offering built-in and ready-to-use functionalities, as well as a multitude of programming approaches. This allows for a multitude of applications including rapid algorithm development, system verification, rapid prototyping, and deployment for cases such as safety/remote monitoring, training and education, hardware control, and factory automation simulation.

995 citations


Proceedings ArticleDOI
16 May 2016
TL;DR: This paper takes the leap of increasing the available training data to 40 times more than prior work, leading to a dataset size of 50K data points collected over 700 hours of robot grasping attempts, which allows us to train a Convolutional Neural Network for the task of predicting grasp locations without severe overfitting.
Abstract: Current model free learning-based robot grasping approaches exploit human-labeled datasets for training the models. However, there are two problems with such a methodology: (a) since each object can be grasped in multiple ways, manually labeling grasp locations is not a trivial task; (b) human labeling is biased by semantics. While there have been attempts to train robots using trial-and-error experiments, the amount of data used in such experiments remains substantially low and hence makes the learner prone to over-fitting. In this paper, we take the leap of increasing the available training data to 40 times more than prior work, leading to a dataset size of 50K data points collected over 700 hours of robot grasping attempts. This allows us to train a Convolutional Neural Network (CNN) for the task of predicting grasp locations without severe overfitting. In our formulation, we recast the regression problem to an 18-way binary classification over image patches. We also present a multi-stage learning approach where a CNN trained in one stage is used to collect hard negatives in subsequent stages. Our experiments clearly show the benefit of using large-scale datasets (and multi-stage training) for the task of grasping. We also compare to several baselines and show state-of-the-art performance on generalization to unseen objects for grasping.

898 citations


References
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Proceedings ArticleDOI
27 Oct 2003
TL;DR: The authors' open-source robot control software, the Carnegie Mellon Navigation (CARMEN) Toolkit, is described, which chooses not to adopt strict software standards, but to instead focus on good design practices.
Abstract: In this paper we describe our open-source robot control software, the Carnegie Mellon Navigation (CARMEN) Toolkit. The ultimate goals of CARMEN are to lower the barrier to implementing new algorithms on real and simulated robots and to facilitate sharing of research and algorithms between different institutions. In order for CARMEN to be as inclusive of various research approaches as possible, we have chosen not to adopt strict software standards, but to instead focus on good design practices. This paper outlines the lessons we have learned in developing these practices.

389 citations


"ROS: an open-source Robot Operating..." refers background in this paper

  • ...In this section, we will elaborate these philosophies and shows how they influenced the design and implementation of ROS....

    [...]


Journal ArticleDOI
TL;DR: This survey selects and describes nine open source, freely available RDEs for mobile robots, evaluating and comparing them from various points of view, and establishes a conceptual framework of four broad categories encompassing the characteristics and capabilities that an RDE supports.
Abstract: Robotic Development Environments (RDEs) have come to play an increasingly important role in robotics research in general, and for the development of architectures for mobile robots in particular. Yet, no systematic evaluation of available RDEs has been performed; establishing a comprehensive list of evaluation criteria targeted at robotics applications is desirable that can subsequently be used to compare their strengths and weaknesses. Moreover, there are no practical evaluations of the usability and impact of a large selection of RDEs that provides researchers with the information necessary to select an RDE most suited to their needs, nor identifies trends in RDE research that suggest directions for future RDE development. This survey addresses the above by selecting and describing nine open source, freely available RDEs for mobile robots, evaluating and comparing them from various points of view. First, based on previous work concerning agent systems, a conceptual framework of four broad categories is established, encompassing the characteristics and capabilities that an RDE supports. Then, a practical evaluation of RDE usability in designing, implementing, and executing robot architectures is presented. Finally, the impact of specific RDEs on the field of robotics is addressed by providing a list of published applications and research projects that give concrete examples of areas in which systems have been used. The comprehensive evaluation and comparison of the nine RDEs concludes with suggestions of how to use the results of this survey and a brief discussion of future trends in RDE design.

271 citations


"ROS: an open-source Robot Operating..." refers background in this paper

  • ...…meet these challenges, many robotics researchers, including ourselves, have previously created a wide variety of frameworks to manage complexity and facilitate rapid prototyping of software for experiments, resulting in the many robotic software systems currently used in academia and industry [1]....

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Journal ArticleDOI
J. Jackson1
TL;DR: The composition of MSRS is examined, looking generally at its architecture and specifically at its solutions for concurrency, distribution, abstraction, simulation, and programmer interaction.
Abstract: Microsoft robotics studio (MSRS) was publicly released in December 2006 with the explicit goal of providing an industry software standard for robot control. To become a viable standard, several technical challenges needed to be solved. In this article, we examine the composition of MSRS, looking generally at its architecture and specifically at its solutions for concurrency, distribution, abstraction, simulation, and programmer interaction. We also examine briefly the emerging industry and academic adoption of the robotics studio.

238 citations


"ROS: an open-source Robot Operating..." refers background in this paper

  • ...While proprietary environments such as Microsoft Robotics Studio [9] and Webots [10] have many commendable attributes, we feel there is no substitute for a fully open platform....

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  • ...While proprietary environments such as Microsoft Robotics Studio [9] and Webots [10] have many commendable attributes, we feel there is no substitute...

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Proceedings ArticleDOI
19 May 2008
TL;DR: A novel concept for a mobile, 2-armed, 25-degree-of- freedom system with backdrivable joints, low mechanical impedance, and a 5 kg payload per arm is described.
Abstract: The most critical challenge for Personal Robotics is to manage the issue of human safety and yet provide the physical capability to perform useful work. This paper describes a novel concept for a mobile, 2-armed, 25-degree-of- freedom system with backdrivable joints, low mechanical impedance, and a 5 kg payload per arm. System identification, design safety calculations and performance evaluation studies of the first prototype are included, as well as plans for a future development.

186 citations


Proceedings Article
01 Jan 2007
TL;DR: The hardware and software integration frameworks used to facilitate the development of these components and to bring them together for the demonstration of the STAIR 1 robot responding to a verbal command to fetch an item are described.
Abstract: The STanford Artificial Intelligence Robot (STAIR) project is a long-term group effort aimed at producing a viable home and office assistant robot. As a small concrete step towards this goal, we showed a demonstration video at the 2007 AAAI Mobile Robot Exhibition of the STAIR 1 robot responding to a verbal command to fetch an item. Carrying out this task involved the integration of multiple components, including spoken dialog, navigation, computer visual object detection, and robotic grasping. This paper describes the hardware and software integration frameworks used to facilitate the development of these components and to bring them together for the demonstration.

71 citations


"ROS: an open-source Robot Operating..." refers background in this paper

  • ...ROS was designed to meet a specific set of challenges encountered when developing large-scale service robots as part of the STAIR project [2] at Stanford University1 and the Personal Robots Program [3] at Willow Garage,2 but the resulting architecture is far more general than the service-robot and mobile-manipulation domains....

    [...]

  • ...…set of challenges encountered when developing large-scale service robots as part of the STAIR project [2] at Stanford University1 and the Personal Robots Program [3] at Willow Garage,2 but the resulting architecture is far more general than the service-robot and mobile-manipulation domains....

    [...]