D. Fox, W. Burgard, and S. Thrun. Active markov localization for mobile robots. Robotics and Autonomous Systems, 1998. To appear.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....considered pragmatic multi robot map making. Several existing approaches operate in the sonar domain, where 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 ....

D. Fox, W. Burgard, and S. Thrun. Active markov localization for mobile robots. Robotics and Autonomous Systems, 1998. To appear.

....it can be accomplished. An essential aspect of this knowledge is a map. That is, information which enables the robot to estimate where it is, where it s going, and how to get there. To date, many researchers have devoted a significant amount of work to solving these three tasks, given a prior map [22, 38, 16, 33]. A few have tackled the problem of constructing a map semi autonomously[47, 24] and some have attempted the map acquisition problem in a fully autonomous setting [20, 8, 31] Of the latter works, the majority commit to a particular sensing modality (sonar) and assume that the world can be ....

....using a camera [11] This work is significant in that it can be exploited as an alternative to the map representation we will employ in Section 5.2. It should be noted that both the Kalman Filter and Markov based approaches to map building are motivated by previous successes in robot localization [22, 42, 16, 10]. The algorithm for map construction presented by Thrun is satisfying in that no specific representation of the underlying uncertainties is required. However, this approach assumes that the robot has already performed the task of exploration the collection of observations from which to build ....

D. Fox, W. Burgard, and S. Thrun. Active markov localization for mobile robots. Robotics and Autonomous Systems, 1998. To appear.

....observations from the environment while performing other tasks. In our work, we address the active localization problem where the robot can execute actions with the only purpose of gaining information about its location. The active localization problem has been addressed by some authors before [4], 5] 6] 7] 9] Despite the general approach of these works is the same as that of ours (action selection based on entropy) they left large room from improvement. For instance, a problem that remains open is how to efficiently obtain a sensor model of the environment. In some of the ....

....moment, the action u to be executed next is the one with lower H(u) since, the lower the entropy H(u) the more informative the action u is likely to be. B. Implementation The entropy based action selection formalism just described is quite general and similar to that described, for instance, in [4]. However, the localization framework presented in section III allows an efficient implementation of this action selection theory. The basic idea is to exploit the particle filter and the appearance based training set to discretize the double integral of equation 4. First, we discretize the ....

D. Fox, W. Burgard, and S. Thrun. Active Markov localization for mobile robots. Robotics and Autonomous Systems, 25(3-4):195--207, 1998.

.... implemented by several families of SLAM algorithms [11, 18, 5, 3] Region II represents integration of motion control and mapping exemplified by virtually all exploration strategies [10, 20, 16, 17, 14] Region III which integrates localization and motion control is the field of active navigation [8, 6, 15] and sensor management [12] Full integration of all three components [5, 1] in Region IV is the goal of this work and will be referred to as integrated exploration. This paper considers an exploration strategy which balances coverage, accuracy, and the speed of exploration the basic driving ....

....distribution of the localizability metric is calculated o# line using an a priori occupancy grid map. The localizability metric introduced in this paper is intended for use on line and in the context of SLAM. SLAM and Active localization (I III) Active stochastic localization is implemented in [6]. The algorithm selects from a list of possible motion actions by weighing the resulting reduction in localization uncertainty against the associated cost. The task of exploration is not considered. Integrated exploration (IV) Feder et al. 5] is the most closely related work. The vehicle ....

D. Fox, W. Burgard, and S. Thrun. Active Markov localization for mobile robots. Robotics and Autonomous Systems, 1998.

....an enormous impact on broad areas in computer vision. In recent work on active object recognition [2, 5, 11, 12] it has already been shown that sensible selection of viewpoints improves recognition rate, especially in settings, where multiple ambiguities between the objects exist. The work of [8] is another example from robotics, where such ideas have been implemented for self localization tasks. Besides the success in the area of active object recognition no comparable work is known for selecting the right sensor data during object tracking. The advantages are quite obvious: For a ....

D. Fox, W. Burgard, and S. Thrun. Active markov localization for mobile robots. Robotics and Autonomous Systems, 25:195--207, 1998.

....on pattern recognition [15] Related to our work the approach in [8] covers one step of our sequential decision process. 21 In the general area of active vision and action selection information theoretic concepts have been investigated recently. Examples are active localization of robots [9], active view point selection for object recognition [1] and sensor planning for active object search [23] The most rigorous application of information theory in image processing and computer vision can be found in [10] The image formation process in 3D is completely embedded in an information ....

D. Fox, W. Burgard, and S. Thrun. Active markov localization for mobile robots. Technical report, Carnegie Mellon University, 1998. 34

....that depends on the number of grid cells. Moreover, robots based solely on proximity sensors get easily confused because the sensors often return similar readings at different locations. Many efforts have been made to solve the problem of uncertainty in robot localization for proximity sensors [3,4]. By comparison, vision is much more informative, since it provides information regarding vhere is what . Nature provides many examples of visual systems that are successful in supporting long range navigation. For example, insects such as bees and ants exhibit remarkable homing abilities, ....

D. Fox, W. Burgard, S. Thrtm, "Active Markov Localization for Mobile Robots", Robotics and Autonomous Systems, 1998.

....motion. ii) The sequence of actions is not reduced to a parametrized trajectory. Then Markov decision processes (MDPs) and partially observable MDPs optimization problems need to be solved [25, 5] The robot actions and the environment are modeled in a statistical framework. Solutions proposed in [21, 5, 8] are based on minimization of a probabilistic metric (entropy) The method developed in [5] is applied under positional uncertainty; the approach proposed here is suitable under both positional and orientation uncertainty. The rest of the paper is structured as follows. Section 2 describes the ....

D. Fox and W. Burgard, Active Markov localization for mobile robots, Robotics and Autonomous Systems 25 (1998), no. 4, 195--207.

....functions [6, 7] To this group belongs the approach, proposed in the present paper. ii) The sequence of actions is not reduced to a parameterized trajectory. Then Markov decision processes (MDPs) and partially observable MDPs optimization problems need to be solved [8] Solutions proposed in [9, 8, 10] are based on minimization of a probabilistic metric (entropy) The method developed in [8] is applied under positional uncertainty; the approach proposed here is suitable under both positional and orientation uncertainty. The rest of the paper is structured as follows. Section 2 describes the ....

D. Fox and W. Burgard, "Active Markov localization for mobile robots," Robotics and Autonomous Systems, vol. 25, no. 4, pp. 195--207, 1998.

....it is optimal in the sense of the reduction in uncertainty and ambiguity. We will demonstrate our approach in an active object recognition scenario. The general problem of optimal sensor data acquisition has been discussed before. Examples can be found in the area of active robot localization [6] and active object recognition [1] where a similar metric has been used, but the sequential implementation is missing. The most related approach, not only from the application point of view, but also from a theoretical point of view is the work of [11] The commonness, differences and ....

D. Fox, W. Burgard, and S. Thrun. Active markov localization for mobile robots. Technical report, Carnegie Mellon University, 1998.

....overcome this drawback, we use a novel form of performance curve, namely a plot of the cumulative cost of the training examples against performance. This is related to the form of the performance curves used by Fox et al. in which performance is plotted against a particular resource, namely time [17]. Performance should be measured as the average performance of the hypotheses generated during a particular iteration of the learning cycle. This is the correct performance measure because it rewards learners which discriminate between competing hypotheses. Another obvious option would be to the ....

....does not exhibit the tradeo between exploration (experimentation) and exploitation. It would do if one sought to maximise gain of information per unit cost. The work in robotic discovery that is perhaps most closely related to ASE Progol is by Fox, Burgard, and Thrun on active localisation [17]. Localisation is the problem of a robot estimating its present position from sensor data. In their approach to localisation, the robot controls its various e ectors so as to most eciently localise itself. More speci cally, they employed a greedy approach analogous to the one proposed by ....

D. Fox, W. Burgard, and S. Thrun. Active Markov localization for mobile robots. Robotics and Autonomous Systems, 25(3-4):195-207, 1998.

....environment; they are detected and recognized using a camera mounted vertically on the observer s platform [1] Natural landmarks could be used as well [8, 15] but would require more sophisticated vision techniques. Proximity sensor readings could be integrated for grid based Markov localization [2, 13], but would require more elaborate ltering techniques. Whenever the observer sees a landmark, it localizes itself in the workspace with a given precision (on the order of 1 2in) that has been experimentally established. When the robot does not see any landmark, the imprecision of its location ....

....imprecision of the estimate of the observer s position (and consequently that of the target) whenever this does not immediately con ict with keeping the target in the observer s eld of view. Robot navigation based on active Markov localization was also achieved using only raw proximity sensors [2, 13] in which case the robot may plan motions for position disambiguation. Landmark based navigation has been addressed from di erent points of view in the literature (e.g. see [4, 23, 25, 19] The principle is simple: if the robot primarily localizes itself relative to landmarks, the planner must ....

D. Fox, W. Burgard, and S. Thrun. Active markov localization for mobile robots. Robotics and Autonomous Systems, 25(3-4):195207, 1998.

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Fox, D., Burgard, W. and Thrun, S. (1998). Active Markov localization for mobile robots, Robotics and Autonomous Systems 25: 195--207.

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Dieter Fox, Wolfram Burgard, and Sebastian Thrun. Active markov localization for mobile robots. Robotics and Autonomous Systems. to appear.

....a comparison of MCL to an alternative localization algorithm capable of global mobile robot localization. In particular, we compared MCL to grid based Markov localization, our previous best stochastic localization algorithm and one of the very few algorithms capable of localizing a robot globally [7,23]. The gridbased localization algorithm relies on a fine grained piecewise constant approximation for the belief Bel, using otherwise identical sensor and motion models. The fact that our implementation employs identical sensor and motion models and is capable of processing the same data greatly ....

D. Fox, W. Burgard, S. Thrun, Active Markov localization for mobile robots, Robotics and Autonomous Systems 25 (3--4) (1998) 195--207.

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D. Fox, W. Burgard, and S. Thrun. Active markov localization for mobile robots. Robotics and Autonomous Systems, 1998. To appear.

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D. Fox, W. Burgard, and S. Thrun. Active markov localization for mobile robots. Robotics and Autonomous Systems, 1998. To appear.

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D. Fox, W. Burgard, and S. Thrun. Active markov localization for mobile robots. Robotics and Autonomous Systems, 1998. To appear.

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D. Fox, W. Burgard, and S. Thrun. Active Markov localization for mobile robots. Robotics and Autonomous Systems (RAS), 25:195--207, 1998.

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Fox, D., Burgard, W., and Thrun, S. 1998. Active markov localization for mobile robots. Robotics and Autonomous Systems.

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D. Fox and W. Burgard. Active markov localization for mobile robots. Robotics and Autonomous Systems, 1998.

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D. Fox, W. Burgard, and S. Thrun. Active Markov localization for mobile robots. Robotics and Autonomous Systems, 25:195--207, 1998.

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D. Fox, W. Burgard, and S. Thrun. Active markov localization for mobile robots. Robotics and Autonomous Systems, 1998. To appear.

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D. Fox, W. Burgard, and S. Thrun, \Active markov localization for mobile robots," tech. rep., Carnegie Mellon University, 1998.

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D. Fox, W. Burgard, and S. Thrun, "Active Markov localization for mobile robots," Robotics and Autonomous Systems, vol. 25, pp. 195--207, 1998.

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