F. Dellaert, W. Burgard, D. Fox, and S. Thrun. Using the condensation algorithm for robust, vision-based mobile robot localization. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE Press, June 1999.  Home/Search   Document Not in Database   Summary   Related Articles   Check

.... the problem of actively servoing a stereo head for landmark acquisition as a robot traverses uneven terrain [Davison and Kita, 2001] Finally, Dellaert et al. take ad vantage of environmental invariants, such as a planar ceiling, to construct a mosaic like map by registering an ensemble of images [Dellaert et al. 1999] . Of particular relevance to this paper is the problem of planning a trajectory for minimizing uncertainty while maximizing the utility of the observed data. MacKay considered the problem of optimally selecting sample points in a Bayesian context for the purposes of inferring an interpolating ....

F. Dellaert, W. Burgard, D. Fox, and S. Thrun. Using the condensation algorithm for robust, vision-based mobile robot localization. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE Press, June 1999.

....uses camera mosaics of the ceiling in addition to the laser scan occupancy map. It uses the EM algorithm to learn the occupancy map and the Markov localization with filter techniques for global localization. The Monte Carlo Localization method based on the CONDENSATION algorithm was proposed in [4]. This vision based Bayesian filtering method uses a sampling based density representation and can represent multi modal probability distributions. Given a visual map of the ceiling obtained by mosaicing, it localizes the robot globally using a scalar brightness measurement. 8] proposed some ....

....facilitate the process considerably. Moreover, when using less specific features, global localization is more di#cult to achieve by just using information from one frame, because multiple possible robot poses may not be reliably di#erentiated. For sonar data in [14] and brightness measurements in [4], stochastic localization methods are required to localize the robot gradually while it moves around. 7 Map Alignment Instead of using only the current frame for global localization, we now build a small sub map of a local region from multiple frames and then align this sub map to the database ....

F. Dellaert, W. Burgard, D. Fox, and S. Thrun. Using the condensation algorithm for robust, vision-based mobile robot localization. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR'99), Fort Collins, CO, June 1999.

....xm x s and dy = y m y s . In pose estimation problems such as uncertainty management can be challenging. In order to estimate the probability distribution function (pdf) of the pose of the moving robot i at time t (P (X i ) we employ a particle filter (Monte Carlo simulation approach: see [7, 3, 11]) The weights of the particles (W i ) at time t are updated using a Gaussian distribution (see Equation 2 where [# i , # i , # i ] has been calculated as in Equation 1 but using the pose of a single particle i (Xm i ) instead of the moving robot pose (Xm ) W i = W t 1 i ....

F. Dellaert, W. Burgard, D. Fox, and S. Thrun. Using the condensation algorithm for robust, vision-based mobile robot localization. In IEEE Comp. Soc. Conf. on Computer Vision & Pattern Recognition. 1999.

....that is both domain and sensor independent. We accomplish this task by posing the problem as one of optimizing the robot s knowledge or information about the world. Most prior work in this domain is constrained to sonar sensing or, where vision sensors are used, a restricted class of models [33, 48, 10, 51, 2]. We seek to generalize the models employed, facilitating a wider domain of environments, and aim to address the open questions posed by other entropy motivated approaches to exploration. The principle contribution of this work will be a theory of exploration which accommodates multiple hypotheses ....

....exponentially with the dimensionality of the parameter space. Furthermore, faithfully simulating the stochastics of a physical system is a di#cult problem in and of itself. Nevertheless, Monte Carlo simulation has been applied successfully to the problems of robot localization and visual tracking[10, 18]. Armed with Bayes Law, and the useful related tools, we now turn our attention to the task of constructing a map of the environment. Note that we are not yet discussing the problem of data acquisition or exploration, but considering what to do once the data has arrived. 3.4. Map Construction in ....

[Article contains additional citation context not shown here]

F. Dellaert, W. Burgard, D. Fox, and S. Thrun. Using the condensation algorithm for robust, vision-based mobile robot localization. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE Press, June 1999.

....abilities for a mobile robot. Robot localization from odometry is affected by large error in the long term. Consequently, other sensors have to be used to deduce the robot position. In some cases [2] range sensors (sonar laser) has been used for this purpose. However, it is widely recognized [3] that the proper solution to the robot localization problem requires the use richer sources of information such as cameras (alone or, most probably, in combination with simpler sensors) In previous work at our group, an omnidirectional camera has been used to determine the location of the robot ....

....time than previous works mainly based on dense probabilistic grids discretizing the whole configuration space of the robot. Despite the popularity of particle filters they have not been used for active localization and few times for localization using vision. In the cases in which vision is used [3], it is done in a very simple way (for instance, just checking for the brighter point in a small area of the image) Our appearance based framework extracts more information from each image and, as already suggested in [3] the resulting localization system would converge in less iterations. ....

[Article contains additional citation context not shown here]

F. Dellaert, W. Burgard, D. Fox, and S. Thrun. Using the condensation algorithm for robust, visionbased mobile robot localization. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'99), June 1999.

....uses camera mosaics of the ceiling in addition to the laser scan occupancy map. It uses the EM algorithm to learn the occupancy map and the Markov localization with filter techniques for global localization. The Monte Carlo Localization method based on the CONDENSATION algorithm was proposed in [4]. This vision based Bayesian filtering method uses a sampling based density representation and can represent multi modal probability distributions. Given a visual map of the ceiling obtained by mosaicing, it localizes the robot globally using a scalar brightness mea surement. 8] proposed some ....

....the process considerably. Moreover, when using less specific features, global localization is more difficult to achieve by just using information from one frame, because multiple possible robot poses may not be reliably differentiated. For sonar data in [14] and brightness measurements in [4], stochastic localization methods are required to localize the robot gradually while it moves around. Map Alignment Instead of using only the current frame for global localization, we now build a small sub map of a local region from multiple frames and then align this sub map to the database ....

F. Dellaert, W. Burgard, D. Fox, and S. Thrun. Using the condensation algorithm for robust, vision-based mobile robot localization. In Proceedings of IEEE Conference on Computer Vision and Pattern Recog- nition (CVPR'99), Fort Collins, CO, June 1999.

....in a hotel, is still solely based on sonar sensors and a laser scanner and navigates with a built in map [15] The goal of another project PRIAMOS is, used to investigate problems like active perception, exploration, and machine learning based on the fusion of various sensor modalities. MINERVA s [5] localization tasks as a museum s guide are solved by the CONDENSATION algorithm using visual data and it s counterpart RHINO [4] besides doing also tour guides, is capable of finding and fetching previously learned objects which have been presented in front of its cameras. This work was ....

F. Daellert, W. Burgard, D. Fox, and S. Thrun. Using the condensation alorithm for robus, vision--based mobile robot localization. Technical report, Computer Science Dept., Carnegie Mellong University, Pittsburgh, 1998.

....from images is frequently addressed by the robotics, the computer vision, and the photogrammetry communities. Due to the huge number of publications, a comprehensive review would be beyond the scope of this paper. Recently sample based versions of Markov localization became very popular [2, 5, 3, 6]. Closely related to sample based Markov localization is the CONDENSATION algorithm [1] which is often used for object tracking. Both approaches represent the posterior distribution of the sought parameters (e.g. the pose) by samples, which allows to approximate virtually any distribution. ....

DELLAERT, F., BURGARD, W., FOX, D., AND THRUN, S. Using the condensation algorithm for robust, vision-based mobile robot localization. In Proc. of IEEE Conference on Computer Vision and Pattern Recognition (1999), pp. 11:588-594.

.... Crowley, as well as Nayar et al. have examined an appearancebased model of the environment and perform localization by interpolation in the manifold of principal components[8, 5] In other work, Dellaert et al. have demonstrated the feasibility of employing a vision sensor in the Markov framework[2]. However, the model of the environment is reduced to a simple overhead planar mosaic, and the sensor model is reduced to a single intensity measurement at each camera location. While these approaches demonstrate the utility of appearance based modeling, they su#er due to the dependency of the ....

F. Dellaert, W. Burgard, D. Fox, and S. Thrun. Using the condensation algorithm for robust, vision-based mobile robot localization. In Computer Vision and Pattern Recognition. IEEE Press, June 1999.

....camera mosaics of the ceiling in addition to the laser scan occupancy map. It uses the EM algorithm to learn the occupancy map and the Markov localization with filter techniques for global localization [8] The Monte Carlo Localization method based on the CONDENSATION algorithm was proposed in [7]. This vision based Bayesian filtering method uses a sampling based density representation. Unlike the Kalman filter based approaches, it can represent multimodal probability distributions. Given a visual map of the ceiling obtained by mosaicing, it localizes the robot globally using a scalar ....

F. Dellaert, W. Burgard, D. Fox, and S. Thrun. Using the condensation algorithm for robust, vision-based mobile robot localization. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR'99), Fort Collins, CO, June 1999.

....the world. Furthermore, a representation of the robot s environment is essential to the tasks of teleoperation and debugging remote systems. Examples of useful representations include measures of radiation hot spots, magnetic declination, sonar and other range based representations, and visual maps[1, 2, 3]. Of these representations, visual maps o er signi cant advantages in terms of the richness of the sensor output, the potential for constructing low cost systems and the utility of the map for application to human oriented problems such as virtual environment representation. We are interested, in ....

F. Dellaert, W. Burgard, D. Fox, and S. Thrun, \Using the condensation algorithm for robust, vision-based mobile robot localization", in IEEE Computer Society Conference on Computer Vision and Pattern Recognition. June 1999, IEEE Press.

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Dellaert, F., Burgard, W., Fox, D. and Thrun, S. (1999a). Using the condensation algorithm for robust, vision-based mobile robot localization, Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, IEEE, Fort Collins, CO.

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F. Dellaert, W. Burgard, D. Fox, and S. Thrun. Using the Condensation algorithm for robust, vision-based mobile robot localization. In Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 1999.

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F. Dellaert, W. Burgard, D. Fox, and S. Thrun. Using the condensation algorithm for robust, vision-based mobile robotlocalization. In Proc. of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1999.

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Frank Dellaert, Wolfram Burgard, Dieter Fox, and Sebastian Thrun, \Using the condensation algorithm for robust, vision-based mobile robot localization," in Proceedings of CVPR-99, 1999.

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Frank Dellaert, Wolfram Burgard, Dieter Fox, and Sebastian Thrun, \Using the condensation algorithm for robust, vision-based mobile robot localization," in Proceedings of CVPR-99, 1999.

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F. Dellaert, W. Burgard, D. Fox, , and S. Thrun. Using the condensation algorithm for robust, vision-based mobile robot localization. In Proc. of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), 1999.

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F. Dellaert, W. Burgard, D. Fox, and S. Thrun. Using the condensation algorithm for robust, vision-based mobile robot localization. In Proc. of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), 1999.

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F. Dellaert, W. Burgard, D. Fox, and S. Thrun. Using the condensation algorithm for robust, vision-based mobile robotlocalization. In Proc. of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1999.

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F. Dellaert, W. Burgard, D. Fox, , and S. Thrun. Using the condensation algorithm for robust, vision-based mobile robot localization. In Proc. of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), 1999.

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F. Dellaert, W. Burgard, D. Fox, and S. Thrun. Using the condensation algorithm for robust, vision-based mobile robotlocalization. In Proc. of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1999.

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Frank Dellaert, Wolfram Burgard, Dieter Fox, and Sebastian Thrun, \Using the condensation algorithm for robust, vision-based mobile robot localization," in Proceedings of CVPR-99, 1999.

....relative to the map. The progression of snapshots in Figure 1 illustrate the development of the particle filter approximation over time, from global uncertainty to a well localized robot. In the context of localization, particle filters are commonly known as Monte Carlo localization (MCL) [7, 51]. MCL s original development was motivated by the condensation algorithm [19] a particle filter that enjoyed great popularity in computer vision applications. In most variants of the mobile localization problem, particle filters have been consistently found to outperform alternative techniques, ....

F. Dellaert, W. Burgard, D. Fox, and S. Thrun. Using the condensation algorithm for robust, vision-based mobile robot localization. In CVPR-99.

....enables us to maintain a full posterior over all poses. On the other hand, calculating (25) requires integration, whereas (17) does not. Luckily, the integration is well understood in the rich literature on Markov localization, and below we will adopt one of the representations developed there [18, 27, 48], as described in the next section. Knowledge of the posterior gives us a better way to calculate the maximum likelihood pose s t . In particular, s t is obtained by maximizing the posterior given by (25) instead of the marginal posterior speci ed in Equation (17) s t = argmax ....

....implements the posterior calculation using particle lters [22, 23, 52, 66] Particle lters apply Rubin s idea of importance sampling [72] to Bayes lters. The resulting algorithm is known in computer vision as condensation algorithm [38] and in mobile robotics as Monte Carlo localization (MCL) [18, 27, 48]. A similar algorithm has been proposed in the context of Bayes networks as [42] survival of the ttest. Particle lters represent the posteriors by a set of particles (samples) Each particle is a pose that represents a guess as to where the robot might be. Particle lters weigh each particle by ....

[Article contains additional citation context not shown here]

F. Dellaert, W. Burgard, D. Fox, and S. Thrun. Using the condensation algorithm for robust, vision-based mobile robot localization. In Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, Fort Collins, CO, 1999. IEEE.

....implements the posterior calculation using particle filters [13, 14, 38, 51] Particle filters apply Rubin s idea of importance sampling [55] to Bayes filters. The resulting algorithm is known in computer vision as condensation algorithm [26] and in mobile robotics as Monte Carlo localization [10, 17, 34]. A similar algorithm has been proposed in the context of Bayes networks as [29] survival of the fittest. Particle filters represent the posteriors by a set of particles (samples) Each particle is a pose that represents a guess as to where the robot might be. Particle filters weigh each ....

....in the existing map. The robot on the left found its pose in (b) and then maintains a sense of location in (c) natural way to solve the problem of determining one robot s pose relative to another is to globally localize one of the robots in the map built by the other robot. It is well known [10, 18] that Monte Carlo localization (MCL) which our approache uses for posterior pose estimation can solve the global localization problem. It does so under one restrictive assumption: That the robot actually be inside the map in which it is being localized. While this assumption is trivially the ....

F. Dellaert, W. Burgard, D. Fox, and S. Thrun. Using the condensation algorithm for robust, vision-based mobile robot localization. In Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, Fort Collins, CO, 1999. IEEE.

....detail in [39] More recently, we developed an alternative representation which is both more efficient than grids and more accurate. Therefore, we will describe it here. The Monte Carlo localization algorithm (MCL) is a version of Markov localization that uses samples to approximate the belief b [24, 25, 29, 37, 60]. It is based on the SIR algorithm (SIR stands for sampling importance resampling) originally proposed in [82] and is a version of particle filters [30, 31, 64, 75] Similar algorithms are known as condensation algorithm [49, 50] in computer vision, and survival of the fittest in AI [55] The ....

....concentrate on the center region in the museum. However, the symmetry of this region makes it impossible to disambiguate them. Finally, the third diagram in Figure 6 shows the belief a few moments later, where all samples focus on the correct pose. The MCL algorithm is in fact quite efficient [24, 25, 37]; slight modifications of the basic algorithms [60, 106] require as few as 100 samples for reliable localization, consuming only a small fraction of time available on a low end PC. Our implementation is any time [22, 108] meaning that it can adapt to the available computational resources by ....

F. Dellaert, W. Burgard, D. Fox, and S. Thrun. Using the condensation algorithm for robust, vision-based mobile robot localization. In Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, Fort Collins, CO, 1999. IEEE.

....map of the National Museum of American History, which was used as the perceptual model in navigating with a vision sensor. The map was acquired using an RWI B18 robot. 2.7. Robot with upward pointed camera Similar results were obtained using a camera as the primary sensor for localization [12]. To test MCL under challenging real world conditions, we evaluated it using data collected in a populated museum. During a two week exhibition, our robot Minerva (Fig. 8) was employed as a tour guide in the Smithsonian s Museum of Natural History, during which it traversed more than 44 km [71] ....

....32 when compared to the case where plain MCL is augmented with uniform samples. These results are significant at the 95 confidence level. We also compared Mixture MCL in the context of visual localization, using only camera imagery obtained with the robot Minerva during public museum hours [12]. Notice that this data set is not the same as the one used above; in particular, it contains an unexplained, unnaturally large odometry error, which occurred for unknown reasons. In this particular case, the odometry reported back by the robot s low level motion controller jumped by a large ....

F. Dellaert, W. Burgard, D. Fox, S. Thrun, Using the condensation algorithm for robust, vision-based mobile robot localization, in: Proc. IEEE International Conference on Computer Vision and Pattern Recognition, Fort Collins, CO, 1999.

....position of the robot. Whereas all these approaches use sophisticated feature matching techniques, they are not applying any filtering techniques to represent a belief of the robot about its current position and to update this belief whenever new measurements arrive and when the robot moves. In [3] the images obtained by the robot are matched to a ceiling mosaic by comparing grey values. The mosaic has to be constructed in advance, which itself is a complex problem. The key contribution lies in the proposed probabilistic method for mobile robot pose estimation denoted as Monte Carlo ....

....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 belief generally is realized by the following two alternating steps: 1. In the prediction step, we draw for each sample a new sample according to the weight of the sample and according to the model p(l a, l # ) of the robot s dynamics given the ....

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F. Dellaert, W. Burgard, D. Fox, and S. Thrun. Using the condensation algorithm for robust, vision-based mobile robot localization. Proc. of the International Conference on Computer Vision and Pattern Recognition (CVPR), 1999.

....the robot is at location x t . For proximity sensors such as sonar sensors, this probability can be computed from the map using ray tracing and a model of the sensor uncertainty (see also [1, 8] Particle filters have been applied with great practical success to a variety of mobile robot systems [7, 3, 16, 6, 12]. Fig. 1 illustrates the application of particle filters to mobile robot localization. Shown there is a map of a hallway environment along with a sequence of sample sets during global localization. The pictures demonstrate the ability of particle filters to represent a wide variety of ....

F. Dellaert, W. Burgard, D. Fox, and S. Thrun. Using the condensation algorithm for robust, vision-based mobile robot localization. In Proc. of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), 1999.

....that uses samples (particles) for representing probability densities. MCL is a version of particle filters [4, 10, 12, 15] In computer vision, particle filters are known under the name condensation algorithm [9] They have been applied with great practical success to visual tracking problems [9, 2] and mobile robot localization [3, 6, 11] The basic idea of MCL is to approximate probability distributions by sets of samples. When applied to the problem of state estimation in a partially observable dynamical system, MCL successively calculates weighted sets of samples that approximate the ....

....as a museum tour guide in the Smithsonian s Museum of National History [18] The data contains logs of odometry measurements and sensor scans taken by Minerva s two laser range finders. Figure 8 shows part of the map of the museum and the path of the robot used for this experiment. As reported in [2, 3, 6], conventional MCL reliably succeeds in localizing the robot. To test our new approach under even harder conditions, we repeatedly introduced errors into the odometry information. These errors made the robot lose track of its position with probability of 0.01 when advancing one meter. The ....

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F. Dellaert, W. Burgard, D. Fox, and S. Thrun. Using the condensation algorithm for robust, vision-based mobile robot localization. CVPR-99.

....In practice, localization approaches using Kalman filters typically require that the starting position of the robot is known. In addition, Kalman filters rely on sensor models that generate estimates with Gaussian uncertainty which is often unrealistic (see for example Dellaert et al. [31]) Recently, Jensfelt and Kristensen [32] introduced an approach based on multiple hypothesis tracking, which allows to model typical probability distributions as they occur during global localization. Topological Markov localization. To overcome the limitations of Kalman filter techniques, ....

F. Dellaert, W. Burgard, D. Fox, and S. Thrun. Using the condensation algorithm for robust, vision-based mobile robot localization. In Proc. of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), 1999.

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F. Dellaert, W. Burgard, D. Fox, and S. Thrun. Using the condensation algorithm for robust, vision-based mobile robot localization. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE Press, June 1999.

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F. Dellaert, W. Burgard, D. Fox, and S. Thrun. Using the CONDENSATION algorithm for robust, vision-based mobile robot localization. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pages 2588--2593, Ft. Collins, CO, June 1999. IEEE Press.

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