Learning Occupancy Grid Maps with Forward Sensor Models |
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Authors: | Sebastian Thrun |
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Affiliation: | (1) Computer Science Department, Stanford University, Stanford, CA 94305, USA |
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Abstract: | This article describes a new algorithm for acquiring occupancy grid maps with mobile robots. Existing occupancy grid mapping algorithms decompose the high-dimensional mapping problem into a collection of one-dimensional problems, where the occupancy of each grid cell is estimated independently. This induces conflicts that may lead to inconsistent maps, even for noise-free sensors. This article shows how to solve the mapping problem in the original, high-dimensional space, thereby maintaining all dependencies between neighboring cells. As a result, maps generated by our approach are often more accurate than those generated using traditional techniques. Our approach relies on a statistical formulation of the mapping problem using forward models. It employs the expectation maximization algorithm for searching maps that maximize the likelihood of the sensor measurements. |
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Keywords: | mobile robotics mapping Bayesian techniques probabilistic inference robot navigation SLAM |
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