D.Fox, W.Burgard, H.Kruppa, S.Thrun. A Monte Carlo algorithm for multirobot localization. Techn. Rep. Carnegie Mellon University, CMU-CS-99-120, 1999.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....for the cooperative localization of a system consisting of n mobile robots. Many results are available on the self localization of single mobile robots (e.g. Fox et al. 1998, Borghi, Caglioti 1998] Recently, attention has been focused towards to multi robot localization: in the work of [Thrun et al. 1999] only partial information is maintained (the socalled factorial representation) about the pose of the robots. In this section, complete information about robot pose is maintained and updated through an efficient distributed process: this is made possible by adopting Gaussian distributions for the ....

D.Fox, W.Burgard, H.Kruppa, S.Thrun. A Monte Carlo algorithm for multirobot localization. Techn. Rep. Carnegie Mellon University, CMU-CS-99-120, 1999.

....purposes. The Gaussian distribution shown in Figure 2 models the error in the estimation of a robot s location. Here the x axis represents the angular error, and the y axis the distance error. This Gaussian has been obtained through maximum likelihood estimation based on training data (see [13] for more details) As is easy to be seen, the Gaussian is zerocentered along both dimensions, and it assigns low likelihood to large errors. Please note that our detection model additionally considers a 6.9 chance to erroneously detecting a robot when there is none. Marian Robin Path Fig. 3: ....

D. Fox, W. Burgard, H. Kruppa, and S. Thrun. A monte carlo algorithm for multi-robot localization. Technical Report CMS-CS-99-120, Carnegie Mellon University, 1999.

.... though all experimental results discussed here use pre recorded data sets (to facilitate the analysis) all evaluations have been performed strictly under run time conditions (unless explicitly noted) In fact, we have routinely ran cooperative teams of mobile robots using MCL for localization (Fox et al. 1999). Comparison to Grid Based Localization The first series of experiments illustrates different capabilities of MCL and compares it to grid based Markov localization, which presumably is the most accurate Markov localization technique to date (Burgard et al. 1996; 1998b; Fox 1998) 10 15 20 25 30 ....

....6 were not generated in real time. As shown there, the accuracy increases with the resolution of the grid, both for sonar (solid line) and for laser data (dashed line) However, grid sizes below 8 cm do not permit updating in real time, even when highly efficient, selective update schemes are used (Fox, Burgard, Thrun 1999). Results for MCL with fixed sample set sizes are shown in Figure 6 (b) These results have been generated using realtime conditions. Here very small sample sets are disadvantageous, since they infer too large an error in the approximation. Large sample sets are also disadvantageous, since ....

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Fox, D.; Burgard, W.; Kruppa, H.; and Thrun, S. 1999. A monte carlo algorithm for multi-robot localization. TR CMU-CS-99120, Carnegie Mellon University.

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D.Fox, W.Burgard, H.Kruppa, S.Thrun. A Monte Carlo algorithm for multirobot localization. Techn. Rep. Carnegie Mellon University, CMU-CS-99-120, 1999.

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