An open problem in Simultaneous Localization and Mapping (SLAM) is the development of algorithms which scale with the size of the environment. A few promising methods exploit the key insight that representing the post...
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ISBN:
(纸本)9783540481102
An open problem in Simultaneous Localization and Mapping (SLAM) is the development of algorithms which scale with the size of the environment. A few promising methods exploit the key insight that representing the posterior in the canonical form parameterized by a sparse information matrix provides significant advantages regarding computational efficiency and storage requirements. Because the information matrix is naturally dense in the case of feature-based SLAM, additional steps are necessary to achieve sparsity. The delicate issue then becomes one of performing this sparsification in a manner which is consistent with the original distribution. In this paper, we present a SLAM algorithm based in the information form in which sparseness is preserved while maintaining consistency. We describe an intuitive approach to controlling the population of the information matrix by essentially ignoring a small fraction of proprioceptive measurements whereby we track a modified version of the posterior. In this manner, the Exactly Sparse Extended Information Filter (ESEIF) performs exact inference, employing a model which is conservative relative to the standard distribution. We demonstrate our algorithm both in simulation as well as on two nonlinear datasets, comparing it against the standard EKF as well as the Sparse Extended Information Filter (SEIF) by Thrun et al. The results convincingly show that our method yields conservative estimates for the robot pose and map which are nearly identical to those of the EKF in comparison to the SEIF formulation which results in overconfident error bounds.
Learning patterns of human behavior from sensor data is extremely important for high-level activity inference. We show how to extract a person's activities and significant places from traces of GPS data. Our syste...
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ISBN:
(纸本)9783540481102
Learning patterns of human behavior from sensor data is extremely important for high-level activity inference. We show how to extract a person's activities and significant places from traces of GPS data. Our system uses hierarchically structured conditional random fields to generate a consistent model of a person's activities and places. In contrast to existing techniques, our approach takes high-level context into account in order to detect the significant locations of a person. Our experiments show significant improvements over existing techniques. Furthermore, they indicate that our system is able to robustly estimate a person's activities using a model that is trained from data collected by other persons.
In the software community, a framework indicates an integrated set of domainspecific software components [CS95] which can be reused to create applications. A framework is more than a library of software components: It...
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Haptic virtual fixtures are software-generated force and position signals applied to human operators in order to improve the safety, accuracy, and speed of robot-assisted manipulation tasks. Virtual fixtures are effec...
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ISBN:
(纸本)9783540481102
Haptic virtual fixtures are software-generated force and position signals applied to human operators in order to improve the safety, accuracy, and speed of robot-assisted manipulation tasks. Virtual fixtures are effective and intuitive because they capitalize on both the accuracy of robotic systems and the intelligence of human operators. In this paper, we discuss the design, analysis, and implementation of two categories of virtual fixtures: guidance virtual fixtures, which assist the user in moving the manipulator along desired paths or surfaces in the workspace, and forbidden-region virtual fixtures, which prevent the manipulator from entering into forbidden regions of the workspace. Virtual fixtures are analyzed in the context of both cooperative manipulation and telemanipulation systems, considering issues related to stability, passivity, human modeling, and applications.
This paper overviews a robotics project at the Expo 2005. The project consists of long term experimental evaluation of practical robots at the Expo site simulating the society in the future and short term demonstratio...
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ISBN:
(纸本)9783540481102
This paper overviews a robotics project at the Expo 2005. The project consists of long term experimental evaluation of practical robots at the Expo site simulating the society in the future and short term demonstration of prototype robots. The long term evaluation can let robots advance from the demonstration level to the practical use one, and the short term demonstration from the single shot experiment level to the demonstration one.
Robot localization is the problem of how to estimate a robot's pose within an objective frame of reference. Traditional localization requires knowledge of two key conditional probabilities: the motion and sensor m...
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ISBN:
(纸本)9783540481102
Robot localization is the problem of how to estimate a robot's pose within an objective frame of reference. Traditional localization requires knowledge of two key conditional probabilities: the motion and sensor models. These models depend critically on the specific robot as well as its environment. Building these models can be time-consuming, manually intensive, and can require expert intuitions. However, the models are necessary for the robot to relate its own subjective view of sensors and motors to the robot's objective pose. In this paper we seek to remove the need for human provided models. We introduce a technique for subjective localization, relaxing the requirement that the robot localize within a global frame of reference. Using an algorithm for action-respecting non-linear dimensionality reduction, we learn a subjective representation of pose from a stream of actions and sensations. We then extract from the data natural motion and sensor models defined for this new representation. Monte Carlo localization is used to track this representation of the robot's pose while executing new actions and receiving new sensor readings. We evaluate the technique in a synthetic image manipulation domain and with a mobile robot using vision and laser sensors.
This paper presents the author's view on the main challenges for autonomous operation in surface mining environment. A brief overview of the mine operation is presented showing the number of components that needs ...
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ISBN:
(纸本)9783540481102
This paper presents the author's view on the main challenges for autonomous operation in surface mining environment. A brief overview of the mine operation is presented showing the number of components that needs to interact in a safe, robust and efficient manner. Successful implementation of autonomous systems in field robotic applications are presented with a discussion of the fundamental problems that needs to be addressed to have this technology accepted in mining operations.
In recent years there is an increasing interest in building personal robots that are capable of a human-like interaction. In addition to multi-modal interaction skills, such a robot must also be able to adapt itself t...
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Why are probabilistic roadmap (PRM) planners "probabilistic"? This paper tries to establish the probabilistic: foundations of PRM planning and re-examines previous work in this context. It shows that the suc...
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ISBN:
(纸本)9783540481102
Why are probabilistic roadmap (PRM) planners "probabilistic"? This paper tries to establish the probabilistic: foundations of PRM planning and re-examines previous work in this context. It shows that the success of PRM planning depends mainly and critically on the assumption that the configuration space C of a robot often verifies favorable "visibility" properties that are not directly dependent on the dimensionality of C. A promising way of speeding up PRM planners is to extract partial knowledge on such properties during roadmap construction and use this knowledge to adjust the sampling measure continuously. This paper also shows that the choice of the sampling source-pseudo-random or deterministic-has small impact on a PRM planner's performance, compared to that of the sampling measure. These conclusions are supported by both theoretical arguments and empirical results.
The advent of social robots increases significantly the number and the kind of robotics systems to be controlled. Since CSRS software depends on the particular hardware architecture of a robot, software reuse becomes ...
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