High-accuracy 3D sensing for mobile manipulation: Improving object detection and door opening

Quigley, Morgan; Batra, Siddharth; Gould, Stephen J.; Klingbeil, Ellen; Le, Quoc V.; Wellman, Ashley; Ng, Andrew Y.

doi:10.1109/robot.2009.5152750

Cited by 63 publications

(47 citation statements)

References 13 publications

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“…The key feature of CNN-based algorithms is that they do not require prior feature extraction, thus resulting in higher generalization capabilities. Recently, CNNs have been shown to be successful in object recognition [17,18], object detection [19], scene parsing [20,21] and scene classification [22][23][24]. In this The remainder of the paper is organized as follows.…”

Section: Introductionmentioning

confidence: 99%

Classification and Segmentation of Satellite Orthoimagery Using Convolutional Neural Networks

et al. 2016

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Abstract:The availability of high-resolution remote sensing (HRRS) data has opened up the possibility for new interesting applications, such as per-pixel classification of individual objects in greater detail. This paper shows how a convolutional neural network (CNN) can be applied to multispectral orthoimagery and a digital surface model (DSM) of a small city for a full, fast and accurate per-pixel classification. The predicted low-level pixel classes are then used to improve the high-level segmentation. Various design choices of the CNN architecture are evaluated and analyzed. The investigated land area is fully manually labeled into five categories (vegetation, ground, roads, buildings and water), and the classification accuracy is compared to other per-pixel classification works on other land areas that have a similar choice of categories. The results of the full classification and segmentation on selected segments of the map show that CNNs are a viable tool for solving both the segmentation and object recognition task for remote sensing data.

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Section: Introductionmentioning

confidence: 99%

Classification and Segmentation of Satellite Orthoimagery Using Convolutional Neural Networks

et al. 2016

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“…Flash ladars (Anderson et al, 2005) often have poor depth and spatial resolution and have non-Gaussian error characteristics that are difficult to deal with. Line stripe systems (Curless and Levoy, 1995;Quigley et al, 2009) have the requisite resolution but neither achieve 15 Hz operation nor deal with moving objects. Stereo systems that employ matching algorithms to produce dense results (Brox et al, 2010;Konolige, 1997;Wedel et al, 2008) can be a suitable sensor for our application.…”

Section: Related Workmentioning

confidence: 99%

Approaches to Probabilistic Model Learning for Mobile Manipulation Robots

Sturm¹

2013

Springer Tracts in Advanced Robotics

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Mobile manipulation robots are envisioned to provide many useful services both in domestic environments as well as in the industrial context. Examples include domestic service robots, that implement large parts of the housework, and versatile industrial assistants, that provide automation, transportation, inspection, and monitoring services. The challenge in these applications is that the robots have to function under changing, real-world conditions, be able to deal with considerable amounts of noise and uncertainty, and operate without the supervision of an expert. To meet these challenges, current robotic systems are typically custom-tailored to specific applications in welldefined environments, and therefore cannot deal robustly with changes in the situation. This thesis presents novel learning techniques that enable mobile manipulation robots, i.e., mobile platforms with one or more robotic manipulators, to autonomously adapt to new or changing situations. The developed approaches in this thesis cover the following four topics: (1) learning the robot's kinematic structure and properties using actuation and visual feedback, (2) learning about articulated objects in the environment in which the robot is operating, (3) using tactile feedback to augment the visual perception, and (4) learning novel manipulation tasks from human demonstrations.In the first part of this thesis, we present innovative approaches to learning a robot's own body schema from scratch using visual self-observation. This allows manipulation robots to calibrate themselves automatically and to adapt their body schemata autonomously, for example after hardware failures or during tool use. In the second part, we extend the developed framework to learning about articulated objects -such as doors and drawers -with which service robots often need to interact. The presented algorithms enable robots to learn accurate kinematic models of articulated objects, which in turn allow them to interact with the objects robustly. In the third part, we provide approaches that allow manipulation robots to make use of tactile perception -an ability that is known to play an important role in human object manipulation skills. The main contributions in this part are approaches to identifying objects and to perceiving aspects of their internal states. With this, a manipulation robot can verify that it has grasped the correct object and, for example, discriminate full from empty bottles. Finally, we present an integrated system that allows human operators to intuitively teach a robot novel manipulation tasks by demonstration.All techniques developed in the thesis are based on probabilistic learning and inference. They have been implemented and evaluated on real robots as well as in simulation. Extensive experiments have been conducted to analyze and validate the properties of the developed algorithms and to demonstrate a significant increase in robustness, adaptability, and utility of mobile manipulation robots in everyday life. Zusammenfassung

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“…The combination of depth information with camera images is addressed in (Quigley et al, 2009). The authors calculate depth information for each pixel in the scene by applying laser-line triangulation with a rotating vertical laser and a camera.…”

Section: Object Classificationmentioning

confidence: 99%

Combined 2D–3D categorization and classification for multimodal perception systems

Márton

Pangercic

Blodow

et al. 2011

The International Journal of Robotics Research

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This article describes an object perception system for autonomous robots performing everyday manipulation tasks in kitchen environments. The perception system gains its strengths by exploiting that the robots are to perform the same kinds of tasks with the same objects over and over again. It does so by learning the object representations necessary for the recognition and reconstruction in the context of pick and place tasks.The system employs a library of specialized perception routines that solve different, well-defined perceptual sub-tasks and can be combined into composite perceptual activities including the construction of an object model database, multi-modal object classification, and object model reconstruction for grasping.We evaluate the effectiveness of our methods, and give examples of application scenarios using our personal robotic assistants acting in a human living environment.

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High-accuracy 3D sensing for mobile manipulation: Improving object detection and door opening

Cited by 63 publications

References 13 publications

Classification and Segmentation of Satellite Orthoimagery Using Convolutional Neural Networks

Classification and Segmentation of Satellite Orthoimagery Using Convolutional Neural Networks

Approaches to Probabilistic Model Learning for Mobile Manipulation Robots

Combined 2D–3D categorization and classification for multimodal perception systems

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