S. Roumeliotis and G. Bekey. Bayesian estimation and kalman filtering: A unified framework for mobile robot localization. In Proc. IEEE Int. Conf. on Robotics & Automation, pg 2985--2992, 2000.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....installed system that can be used to localize a mobile robot in both indoor and outdoor environments. The ability of a robot localize itself is a fundamental problem for mobile robots. Not surprisingly, many technologies and techniques for robot localization can be found in the literature (e.g. [1, 8, 9, 14]) While there are many different variations of the localization problem, we concentrate on three: static localization, position tracking, and simultaneous localization and mapping (SLAM) Static localization requires a robot to obtain an accurate estimate of its global position based only sensor ....

....filter has the advantage that the representation of the distribution is compact; a Gaussian distribution can be represented by a mean and a covariance matrix. Recent extensions to Kalman filtering allow for non Gaussian, multimodal probability distributions through multiple hypothesis tracking [8]. The result is a more versatile estimation technique that still preserves many of the computational advantages of the Kalman filter. Markov methods provide another means of estimation [9] Here, the space of possible robot positions in discretized (often into a probability grid ) and the ....

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S.I. Roumeliotis and G.A. Bekey. Bayesian estimation and kalman filtering: A unified framework for mobile robot localization. In Proceedings of the 2000.

.... it is relatively straightforward to transform observations from a given position to the frame of reference of the other observers thereby exploiting structural relationships in the data ( 10, 5, 1] One approach to the fusion of such data is through the use of Kalman Filtering and its extensions ([15, 14]) In other work, Rekleitis, Dudek and Milios have demonstrated the utility of introducing a second robot to aid in the tracking of the exploratory robot s position ( 12] and introduced the concept of cooperative localization. Recently, several authors have considered using a team of mobile ....

S. Roumeliotis and G. Bekey. Bayesian estimation and kalman filtering: A unified framework for mobile robot localization. In Proc. IEEE Int. Conf. on Robotics & Automation, pg 2985--2992, 2000.

....compensate incremental errors in a robot s odometry. Algorithms for position tracking often make restrictive assumptions on the size of the error and the shape of the robot s uncertainty, required by a range of existing localization algorithms. More challenging is the global localization problem [6,34,61], where a robot is not told its initial pose but instead has to determine it from scratch. The global localization problem is more difficult, since the error in the robot s estimate cannot be assumed to be small. Consequently, a robot should be able to handle multiple, distinct hypotheses. Even ....

....makes plain Kalman filters inapplicable to global localization problems. This limitation is overcome by two related families of algorithms: localization with multi hypothesis Kalman filters and Markov localization. Multi hypothesis Kalman filters represent beliefs using mixtures of Gaussians [9,34,60,61], thereby enabling them to pursue multiple, distinct hypotheses, each of which is represented by a separate Gaussian. However, this approach inherits from Kalman filters the Gaussian noise assumption. To meet this assumption, virtually all practical implementations extract low dimensional features ....

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S.I. Roumeliotis, G.A. Bekey, Bayesian estimation and Kalman filtering: A unified framework for mobile robot localization, in: Proc. IEEE International Conference on Robotics and Automation (ICRA-2000.

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S. I. Roumeliotis and G. A. Bekey. Bayesian estimation and kalman filtering: A unified framework for mobile robot localization. In Proc. of the IEEE Int'l Conf. on Robotics and Automation, pages 2985--2992, San Francisco, CA, April 2000.

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S. I. Roumeliotis and G. A. Bekey. Bayesian estimation and kalman filtering: A unified framework for mobile robot localization. In Proc. of the IEEE Int'l Conf. on Robotics and Automation, pages 2985--2992, San Francisco, CA, April 2000.

....the quality of localization. Uncertainty is the limiting factor in this case. Areas that appear similar prohibit the exteroceptive sensing module to single out a location among a set of possible ones. By using sensors from both categories and combining both approaches in a propagationupdate cycle [14], the exteroceptive sensor uncertainty can be reduced while filtering out the noise in the proprioceptive sensor signals. Exteroceptive sensors can also be used to derive direct estimates of a vehicle s motion (motion from structure) For example, laser scan matching and vision based correlation ....

S. I. Roumeliotis and G. A. Bekey, "Bayesian estimation and kalman filtering: A unified framework for mobile robot localization," in Proceedings of the IEEE International Conference on Robotics and Automation, San Fransisco, CA, April 24-28 2000, pp. 2985--2992.

....robot autonomy[6] Indoors and outdoors robots need to know their exact position and orientation (pose) in order to perform their required tasks. There have been numerous approaches to the localization problem utilizing differenttypes of sensors [7] and avarietyoftechniques (e.g. 5] 4] 15] [20]) The key idea behind most of the current localization schemes is to optimally combine measurements from proprioceptive sensors that monitor the motion of the vehicle with information collected by exteroceptive sensors that provide a representation of the environment and its signals. Many robotic ....

S.I. Roumeliotis and G.A. Bekey.Bayesian estimation and kalman filtering: A unified framework for mobile robot localization. In Proceedings of the 2000 IEEE International ConferenceonRobotics and Automation, pages 2985--2992, San Fransisco, CA, April 24-28 2000.

No context found.

S. Roumeliotis and G. Bekey. Bayesian estimation and kalman filtering: A unified framework for mobile robot localization. In Proc. IEEE Int. Conf. on Robotics & Automation, pg 2985--2992, 2000.

No context found.

Roumeliotis, S.I. and Bekey, G.A. Bayesian estimation and Kalman filtering: A unified framework for mobile robot localization. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pages 2985--2992, San Francisco, CA, 2000.

No context found.

S.I. Roumeliotis and G.A. Bekey. Bayesian estimation and Kalman filtering: A unified framework for mobile robot localization. In Proc. of the IEEE International Conference on Robotics & Automation, 2000.

No context found.

S.I. Roumeliotis and G.A. Bekey. Bayesian estimation and Kalman filtering: A unified framework for mobile robot localization. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pages 2985--2992, San Francisco, CA, 2000. IEEE.

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