C.F. Olson. Probabilistic self-localization for mobile robots. IEEE Transactions on Robotics and Automation, 16(1):55--66, 2000.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....[11] Sensor specific solutions, such as visual tracking [12] can perform local data association very efficiently, but cannot be used to solve the crucial revisiting problem. Alternative approaches search for the best solution in the vehicle pose space rather than in the correspondence space [13] [15] In other approaches, geometric constraints between features are used to obtain hypotheses with pairwise compatible pairings. Baley et al. 16] consider relative distances and angles between points and lines in two laser scans and use a graph theoretic approach to find the largest number ....

C. F. Olson, "Probabilistic self-localization for mobile robots," IEEE Trans. Robot. Automat., vol. 16, pp. 55--66, Feb. 2000.

....Mittal and Davis[3] worked with the unification of input from a wide baseline array of cameras, and used this unification for person tracking. These papers provide some background to the problem of unifying several sensors in the context of human activity modeling. Gern Gilles[9] and Olson[10], among others, used lasers to build OGs. Their work supports the use of laser data for generating occupancy grids. Schulz, Burgard, Fox Cremers[5] Chang Gong[6] and Mahler[8] are among those who have dealt with modeling of human activity embedded in an area. The method we present in this ....

Olson, C.F: "Probabilistic self-localization for mobile robots," IEEE Transactions on Robotics and Automation, Vol. 16, No. 1, pp 55-66, February 2000.

....which are updated with Monte Carlo methods. In contrast with Kalman ltering techniques, these methods can globally localize a robot; with respect to grid based Markov localization, the Monte Carlo methods require less memory and are more accurate. Another probabilistic method is due to Olson [19], that, in order to match the map generated by the robot s sensors with the a priori map of the environment, uses a criteria based on the maximum likelihood similarity measure. In order to nd the position with the best match, he divides the search space into rectilinear cells and apply a ....

Olson, C.F.: Probabilistic self-localization for mobile robots. IEEE Transactions on Robotics and Automation 16 (2000) 55--66

....executed actions. It is one of the fundamental problems in mobile robot navigation and many solutions have been presented in the past including approaches employing Kalman filtering [14, 15, 17, 18] grid based Markov localization [4, 10] or Monte Carlo methods [9, 16, 20] For an overview see [7, 11, 19]. By performing localization experiments with a mobile robot it has been ascertained that grid based Markov localization is more robust than Kalman filtering while the latter given good inputs is more efficient and accurate than the former [13] A combination of both approaches is likely to ....

C. F. Olson. Probabilistic self-localization for mobile robots. IEEE Transactions on Robotics and Automation, 16(1):55--66, Feb. 2000.

....sensor readings and executed actions. It is one of the fundamental problems in mobile robot navigation and many solutions have been presented in the past including approaches employing Kalman filtering [1, 7, 8] grid based Markov localization [3] or Monte Carlo methods [6, 10] For a survey see [9]. By performing localization experiments with a mobile robot it has been ascertained that grid based Markov localization is more robust than Kalman filtering while the latter given good inputs is more accurate than the former [4] A combination of both approaches is likely to inherit the ....

C. F. Olson. Probabilistic self-localization for mobile robots. IEEE Transactions on Robotics and Automation, 16(1):55-- 66, Feb. 2000.

....imagery, including object recognition and stereo matching. We also describe the application of these techniques to optimal feature selection for tracking applications. We have previously used similar techniques to perform localization for mobile robots by matching three dimensional terrain maps [16]. ### ########### #### ######### #### We rst tested these techniques in controlled experiments, in which we could compare the performance of the techniques with precise ground truth. In these experiments, we randomly generated synthetic models containing 60 feature points in a 64#64 pixel unit ....

C. F. Olson, \Probabilistic self-localization for mobile robots", #### ############ ## ######## ### ##########, vol. 16, no. 1, pp. 55-66, Feb. 2000.

....imagery, including object recognition and stereo matching. We also describe the application of these techniques to optimal feature selection for tracking applications. We have previously used similar techniques to perform localization for mobile robots by matching three dimensional terrain maps [16]. 6.1 Experiments with synthetic data We rst tested these techniques in controlled experiments, in which we could compare the performance of the techniques with precise ground truth. In these experiments, we randomly generated synthetic models containing 60 feature points in a 64 64 pixel unit ....

C. F. Olson, \Probabilistic self-localization for mobile robots", IEEE Transactions on Robotics and Automation, vol. 16, no. 1, pp. 55-66, Feb. 2000.

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C.F. Olson. Probabilistic self-localization for mobile robots. IEEE Transactions on Robotics and Automation, 16(1):55--66, 2000.

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Clark F. Olson. Probabilistic self-localization for mobile robots. IEEE Transactions on Robotics and Automation, 16(1):55-66, 2000.

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C.F. Olson. Probabilistic self-localization for mobile robots. IEEE Transactions on Robotics and Automation, 16(1), 2000.

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C. Olson, "Probabilistic self-localization for mobile robots, IEEE Trans. on Robotics & Autom., 16(1): 55-66, Feb 2000.

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C.F. Olson, "Probabilistic Self-Localization for Mobile Robots," IEEE Trans. on Robotics and Automation, vol. 16, pp. 55-66, 2000.

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