B. Schiele and J. Crowley. A comparison of position estimation techniques using occupancy grids. IEEE International Conference on Robotics and Automation, pages 1628-- 1634, 1994.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....from sensor data and matching them with a representation of the environment in terms of these features. Lines are often used for localization in polygonal environment. For instance, in [5] an algorithm for computing robot poses by line matching is presented, while the localization method in [6] is based on certainty grids and on the use of the Hough Transform only for extracting lines from these points. In this paper we present a method called Global Hough Localization, that extends the Hough Localization method described in [7] 8] in order to provide a solution for global ....

....have shown the e ectiveness and eciency of the approach for computing the absolute pose of the robot in polygonal environments. Similar previous works include the method in [5] that presents an algorithm for matching lines extracted by the sensors with reference lines, while the work in [6]isbasedontwo certainty grids (one containing data from the reference map, the other containing data acquired by a range sensor) and uses the Hough Transform for detecting segments from the two grids, while the association problem is addressed by comparing in the Cartesian space each pair of ....

B. Schiele and J. Crowley, \A comparison of position estimation techniques using occupancy grids," ######## ### ########## #######,vol. 12, pp. 163-171, 1994.

....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, ....

B. Schiele and J. Crowley. A comparison of position estimation techniques using occupancy grids. In Proceedings of the 1994.

....Each line indicates the range, as measured by the sensor. Also shown is an outline of the environment, which suggests that the range measurements are of high quality. The measurement model adopted in our implementation is a probabilistic generalization of the rich literature on scan matching [34, 54, 74]. It assumes that each scan induces a local map, which can be conceptually decomposed into three types of areas: free space, occupied space, and occluded space. The same conceptual decomposition applies to the map. Each of the measurements in a range scan z t can thus fall into three di erent ....

B. Schiele and J. Crowley. A comparison of position estimation techniques using occupancy grids. In Proceedings of the 1994.

....Each line indicates the range, as measured by the sensor. Also shown is an outline of the environment, which suggests that the range measurements are of high quality. The measurement model adopted in our implementation is a probabilistic generalization of the rich literature on scan matching [24, 39, 57]. It assumes that each scan induces a local map, which can be conceptually decomposed into three types of areas: free space, occupied space, and occluded space. The same conceptual decomposition applies to the map. Each of the measurements in a range scan z t can thus fall into three different ....

B. Schiele and J. Crowley. A comparison of position estimation techniques using occupancy grids. In Proceedings of the 1994.

....order of difficulty: 1. Position tracking. Here the initial robot pose is known, and the goal of localization is to compensate small odometry error as the robot moves. Typically, the uncertainty in position tracking is local, making unimodal state estimators such as Kalman filters applicable [2, 44, 61, 85]. 5 2. Global localization. If the robot does not know its initial pose, it faces a global localization problem. To localize itself from scratch, a robot must be able to cope with ambiguities and multiple beliefs during localization. 3. Robot kidnapping [35] This problem is a variant of the ....

B. Schiele and J. Crowley. A comparison of position estimation techniques using occupancy grids. In Proceedings of the 1994.

.... the most fundamental problem to providing a mobile robot with autonomous capabilities [8] The mobile robot localization problem comes in many different flavors [4,24] The most simple localization problem which has received by far the most attention in the literature is position tracking [4,64,74,75]. Here the initial robot pose is known, and Corresponding author. E mail address: thrun cs.cmu.edu (S. Thrun) 0004 3702 01 see front matter 2001 Published by Elsevier Science B.V. PII: S0004 3702(01)00069 8 the problem is to compensate incremental errors in a robot s odometry. ....

B. Schiele, J. Crowley, A comparison of position estimation techniques using occupancy grids, in: Proc. 1994.

....represent the beliefs p(l) and p(s) of the robot s position and the state of the object. Over the past years, different techniques have been used to represent the beliefs. Among them are piecewise constant approximations as applied in [3,4,12,15,19,25] A very popular approach is to use Gaussians [10,16,24,26] to represent the densities. In this paper we use particle filters to approximate the involved densities. The key idea of particle filters is to use a sample based representation for the densities. The updates are carried out using resampling techniques (see e.g. 6,11,21] To estimate the ....

B. Schiele, J.L. Crowley, A comparison of position estimation techniques using occupancy grids, in: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), San Diego, CA, 1994.

....distribution. Sensor readings, too, are assumed to map to Gaussian shaped distributions over the robot s position. For these assumptions, Kalman filters provide extremely efficient update rules that can be shown to be optimal (relative to the assumptions) 27] Kalman filter based techniques [28, 29, 30] have proven to be robust and accurate for keeping track of the robot s position. However, these techniques do not represent multi modal probability distributions, which frequently occur during global localization. In practice, localization approaches using Kalman filters typically require that ....

B. Schiele and J.L. Crowley. A comparison of position estimation techniques using occupancy grids. In Proc. of the IEEE International Conference on Robotics & Automation (ICRA), 1994.

....to questions regarding position esD R A F T November 30, 1999, 7:52pm D R A F T 6 timates. The search for the best position estimate can be conducted in two different spaces: 1) the space of correspondences between image and map features [2, 24, 25] or (2) the space of position estimates [7, 8, 21, 40, 49]. The search in the space of position estimates is often conducted by quantizing this space into a two dimensional array, an approach inspired by the occupancy grids developed for map making [13, 14, 34, 35] Position can also be estimated qualitatively: instead of precise geometric estimates, ....

B. Schiele and J. L. Crowley. A comparison of position estimation techniques using occupancy grids. IEEE International Conference on Robotics and Automation, pages 1628--1634, 1994.

....by comparing sensor data, and data obtained from a previously built representation of the environment. Several solutions have been proposed for environment representation. One of them consists of using grid cells, which allows a simple environment representation to be built from sensor data. [4,18,20]. Another solution consists of using topological maps, in which the environment is represented by means of objects and graphs; 6,7,9,14,16] path planning becomes easier, but learning and sensor integration becomes more difficult. Different neural network approaches have been used to maintain the ....

Schiele, B. and Crowley, J., 1994, "A Comparison of Position Estimation Techniques Using Occupancy Grids." Robotics and Autonomous Systems, Vol. 12, pp. 163-171.

....small odometric errors. Thus, they differ from the approach described here in that they require knowledge of the robot s initial position; and they are not able to recover from global localizing failures. Probably the most popular method for tracking a robot s position is Kalman filtering [28, 29, 46, 48, 59, 63], which represents uncertainty by the first and second moments of the density. These approaches are unable to localize robots under global uncertainty a problem which Engelson called the kidnapped robot problem [19] Recently, several researchers proposed Markov localization, which enables ....

B. Schiele and J.L. Crowley. A comparison of position estimation techniques using occupancy grids. In Proc. of the IEEE International Conference on Robotics & Automation (ICRA), 1994.

....specially regarding dynamic environments: i) they can be quickly updated; and ii) small changes in the environment tend to result in small changes in the grid. To minimise the problem instance, instead of working with complete grids most methods search for signi# cant structures in local ones [2][6] but they are usually limited to static environments. Thrun [7] uses evidence grids in dynamic environments, but it is assumed that walls are either parallel or perpendicular to each other and, therefore, the method is not valid for unstructured environments. Yamauchi [8] deals with dynamic ....

....A sonar reading typically provides information about the distance of the sensor to the closest obstacle in the direction of the beam. Since sonars present an arc of uncertainty, most systems rely on accumulating evidence from several ones, which are usually integrated into an evidence grid [6]. If a mobile robot builds an evidence grid while it knows its correct position, it can compare local grids adquired after losing its reference to the global one to locate itself. In order to achieve a grid accurate enough to represent most signi#cant features within a typical indoor environment, ....

Schiele, B. and Crowley, J. "A comparison of position estimation techniques using occupancy grids", Robotics and autonomous systems, 12, pp. 163171, 1994

....order of di#culty: 1. Position tracking. Here the initial robot pose is known, and the goal of localization is to compensate small odometry error as the robot moves. Typically, the uncertainty in position tracking is local, making unimodal state estimators such as Kalman filters applicable [2, 44, 61, 85]. 5 2. Global localization. If the robot does not know its initial pose, it faces a global localization problem. To localize itself from scratch, a robot must be able to cope with ambiguities and multiple beliefs during localization. 3. Robot kidnapping [35] This problem is a variant of the ....

B. Schiele and J. Crowley. A comparison of position estimation techniques using occupancy grids. In Proceedings of the 1994 IEEE International Conference on Robotics and Automation, pages 1628--1634, San Diego, CA, May 1994.

....between adjacent partitions. Then, whenever the robot is in the vicinity of the boundaries of one of the partitions, it uses the correlation of the local grid constructed on line as the robot moves , with the learned local model to correct possible errors of the dead reckoning system [10], 12] ....

B. Schiele and J. Crowley. "A comparison of position estimation techniques using occupancy grids". IEEE Int. Conf. on Robotics and Automation, pp. 1628-- 1634, 1994.

....order of difficulty: 1. Position tracking. Here the initial robot pose is known, and the goal of localization is to compensate small odometry error as the robot moves. Typically, the uncertainty in position tracking is bounded, making unimodal state estimators such as Kalman filters applicable [2, 57, 84, 120]. 5 2. Global localization. If the robot does not know its initial pose, it faces a global localization problem. To localize itself from scratch, a robot must be able to cope with ambiguities and multiple, competing hypotheses during localization. 3. Robot kidnapping [46] This problem is a ....

....the robot in a metric space, just like those methods proposed in [10, 141, 144] The vast majority of approaches is incapable of localizing a robot globally or to recover from robot kidnapping. Instead, they are designed to track the robot s position by compensating small odometric errors [56, 57, 89, 120, 134]. Recently, several researchers proposed the Markov localization used by Minerva, which enables robots to localize themselves under global uncertainty [20, 35, 48, 68, 101, 132] Minerva s and Rhino s localization algorithm goes beyond previous approaches in that it can cope with invisible hazards ....

B. Schiele and J. Crowley. A comparison of position estimation techniques using occupancy grids. In Proceedings of the 1994 IEEE International Conference on Robotics and Automation, pages 1628-- 1634, San Diego, CA, May 1994.

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B. Schiele and J. Crowley. A comparison of position estimation techniques using occupancy grids. IEEE International Conference on Robotics and Automation, pages 1628-- 1634, 1994.

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B. Schiele and J. Crowley, "Comparison of position estimation techniques using occupancy grids," in Proc of International Conference on Robotics and Automation, pp. 1628--1634, May 1994.

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B. Schiele and J. Crowley. A comparison of position estimation techniques using occupancy grids. Robotics and Autonomous Systems, 12:163-171, 1994.

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B. Schiele and J. Crowley. A comparison of position estimation techniques using occupancy grids. Robotics and Autonomous Systems, 12, 1994.

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Bernt Schiele and James L. Crowley. A comparison of position estimation techniques using occupancy grids. In Proceedings of the 1994 IEEE International Conference on Robotics and Automation, pages 1628--1634, San Diego, CA, May 1994.

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Schiele, B. and Crowley, J. A Comparison of Position Estimation Techniques Using Occupancy Grids. in: Proceedings of the 1994.

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B. Schiele and J. L. Crowley, \A comparison of position estimation techniques using occupancy grids," in Proceedings of the International Conference on Robotics and Automation, vol. 2, pp. 1628{ 1634, IEEE, May 1994.

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Bernt Schiele and James L. Crowley. A comparison of position estimation techniques using occupancy grids. In Proc. of the IEEE International Conference on Robotics and Automation, pages 1628--1634, 1994.

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Schiele, B. and Crowley, J., 1994, "A Comparison of Position Estimation Techniques Using Occupancy Grids." Proceedings of IEEE International Conference on Robotics and Automation, San Diego, CA, May 8-13, pp. 1628-1634.

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B. Schiele and J. Crowley. A comparison of position estimation techniques using occupancy grids. In Proceedings of ICRA, 1994.

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