K. Konolige. Markov localization using correlation. In Proc. of the International Joint Conference on Artificial Intelligence (IJCAI), 1999.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....such as localization, path planning, collision avoidance, and people finding. The basic occupancy grid map paradigm has been applied successfully in many different ways. For example, some systems use maps locally, to plan collision free paths or to identify environment features for localization [1, 10, 19, 20]. Others, such as many of the systems described in [11, 23] rely on global occupancy grid maps for global path planning and navigation. f Figure 1: A set of noise free sonar measurements that a robot may receive while passing an open door. While the measurements are perfectly consistent, ....

K. Konolige and K. Chou. Markov localization using correlation. In Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence (IJCAI), Stockholm, Sweden, 1999. IJCAI.

....environment maps overlap. Identifying the environment could then be solved by a global localization approach such as Markov Localization [1] where from the estimated pose the corresponding environment could be determined. Taking this point of view, map correlation as presented by Konolige and Chou [7] could be qualified as a related approach. In their work, the robot builds a patch (a small map) consisting of the last few frames of a laser range finder and correlates this patch with an occupancy grid like map in order to estimate the pose of the robot. A similar approach is the ....

K. Konolige and K. Chou. Markov localization using correlation. In Proc. International Joint Conference on Artificial Intelligence (IJCAI'99), Stockholm, 1999.

....a state of the art localizer in dynamic environments. 1 Introduction In the past decade, mobile robot localization and mapping has received substantial interest in AI and robotics [1, 12] The localization problem concerns itself with estimating the pose of a robot relative to a fixed map [9, 11], whereas the mapping addresses the problem of learning a map from sensor data [4, 13, 16] A striking characteristic of the rich literature on this topic is that virtually all published work assumes that the environment is static. This is in contrast to most robotic environments, which usually ....

K. Konolige and K. Chou. Markov localization using correlation. In Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence (IJCAI), Stockholm, Sweden, 1999. IJCAI.

....robot environments. They represent an environment by a fine grained, metric grid of variables that reflect the occupancy of the environment. Once acquired, they facilitate various key aspects of mobile robot navigation, such as localization, path planning, collision avoidance, and people finding [1, 5, 9]. Existing occupancy grid mapping algorithms suffer a key problem. They often generate maps that are inconsistent with the data, particularly in cluttered environments and when learned from sonar data. This problem is due to the fact that existing algorithms decompose the highdimensional mapping ....

K. Konolige and K. Chou. Markov localization using correlation. Proceedings of IJCAI, 1999

....of Registering Strength Signal Strength Figure 1: Two examples of signal strength distributions, measured over time at a constant location. the existence of a good linearization. Arguably the most powerful algorithms to date are based on Bayesian inference, in particular Markov models [15, 8] and Monte Carlo localization [7, 23] Various discretization schemes can be employed but often occupancy grids and point sets are employed. If no usable theoretical model is available, conditional probability distributions can be sampled directly. Alternately, the environment can be modeled with ....

....off the ground. We had a fairly precise map of the building that we had processed to mark off free space and obstacles. The pixel resolution was roughly six centimeters in this map. Our Model The localizer that we implemented operates in the general framework of Bayesian inference localization [22, 8, 15]. We chose a state space and observation space, estimated the required conditional probability distributions and explicitly integrated. We chose various sets of points in the map for the state space. A point for our experiments was chosen as a tuple############# on the floor of the building our ....

K. Konolige and K. Chou. Markov Localization using Correlation. In Proc. of the Seventeenth International Joint Conference on Artificial Intelligence (IJCAI), pages 1154--1159, Seattle, Washington, August 1999.

....In practice, obtaining linearizations for many sensing systems is difficult and errors can propagate very quickly through the system. Bayesian Approaches. Possibly the most powerful family of global localization algorithms to date is based on Bayesian inference, in particular Markov localization [24, 15] and Monte Carlo localization [13, 37] These are generalizations of the Kalman filter. These algorithms estimate posterior distributions over robot poses which are approximated by piecewise constant functions instead of Gaussians, enabling them to represent highly multi modal distributions. In ....

K. Konolige and K. Chou. Markov localization using correlation. In Proc. of the Seventeenth International Joint Conference on Artificial Intelligence (IJCAI), pages 1154--1159, Seattle, Washington, Aug. 1999.

....In practice, obtaining linearizations for many sensing systems is difficult and errors can propagate very quickly through the system. Bayesian Approaches Possibly the most powerful family of global localization algorithms to date is based in Bayesian inference, in particular Markov localization [23, 14] and Monte Carlo localization [12, 35] These are generalizations of the Kalman filter. These algorithms estimate posterior distributions over robot poses which are approximated by piecewise constant functions instead of Gaussians, enabling them to represent highly multi modal distributions. In ....

K. Konolige and K. Chou. Markov Localization using Correlation. In Proc. of the Seventeenth International Joint Conference on Artificial Intelligence (IJCAI), pages 1154-- 1159, Seattle, Washington, August 1999.

....movement a at position l # . Furthermore, the quantity l) denotes the probability of making observation o given the robot s current location is l. It highly depends on the information the robot possesses about the environment and the sensors used. Different kinds of realizations can be found in [12, 7, 18, 2, 8]. In this paper, p(o l) is computed using the image retrieval system described in Section 3. To represent the belief of the robot about its current position we apply a variant of Markov localization denoted as Monte Carlo localization [3, 5] In Monte Carlo localization, the update of the ....

K. Konolige. Markov localization using correlation. In Proc. of the International Joint Conference on Artificial Intelligence (IJCAI), 1999.

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K. Konolige. Markov localization using correlation. In Proc. of the International Joint Conference on Artificial Intelligence (IJCAI), 1999.

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K. Konolige. Markov localization using correlation. n Proc. of the International Joint Conference on Artificial Intelligence (IJCAI), 1999. 113

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K. Konolige and K. Chou, "Markov localization using correlation," in Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI'99), 1999.

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K. Konolige and K. Chou, "Markov localization using correlation," in Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI'99), 1999.

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K. Konolige and K. Chou. Markov localization using correlation. In Proc. of the International Joint Conference on Artificial Intelligence (IJCAI), 1999.

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K. Konolige and K. Chou. Markov localization using correlation. In Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence (IJCAI), Stockholm, Sweden, 1999. IJCAI.

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K. Konolige and K. Chou, "Markov localization using correlation," in Proc 17th Int. JoYY CoY . Artificial Intelligence, Seattle, WA, Aug. 1999, pp. 1154--1159.

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K. Konolige and K. Chou, "Markov localization using correlation," in Proceedings of the International Joint Conference on Artificial Intelligence, 1999.

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K. Konolige and K. Chou. Markov localization using correlation. In Proceedings of the International Joint Conference on Artificial Intelligence, 1999.

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K. Konolige and K. Chou. Markov localization using correlation. In Proc. of the International Joint Conference on Artificial Intelligence (IJCAI), 1999.

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K. Konolige and K. Chou. Markov localization using correlation. In Proceedings of the International Joint Conference on Artificial Intelligence, 1999.

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K. Konolige. Markov localization using correlation. n Proc. of the International Joint Conference on Artificial Intelligence (IJCAI), 1999. 113

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K. Konolige and K. Chou. Markov localization using correlation. In Proc. Int. Joint Conf. on Artificial Intelligence (IJCAI), pages 1154--1159, 1999.

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K. Konolige and K. Chou. Markov localization using correlation. In Proc. of the International Joint Conference on Artificial Intelligence (IJCAI), 1999.

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K. Konolige and K. Chou. Markov localization using correlation. In International Joint Conference on Articial Intelligence, pages 1154--1159, 1999.

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K. Konolige. Markov localization using correlation. In Proc. of the International Joint Conference on Artificial Intelligence (IJCAI), 1999.

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K. Konolige and K. Chou. Markov localization using correlation. In Proc. of the International Joint Conference on Artificial Intelligence (IJCAI), 1999.

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