publication . Conference object . 1998

An experimental comparison of localization methods

Wolfram Burgard;
Open Access
  • Published: 01 Jan 1998
  • Publisher: IEEE
Abstract
Localization is the process of updating the pose of a robot in an environment, based on sensor readings. In this experimental study, we compare two methods for localization of indoor mobile robots: Markov localization, which uses a probability distribution across a grid of robot poses; and scan matching, which uses Kalman filtering techniques based on matching sensor scans. Both these techniques are dense matching methods, that is, they match dense sets of environment features to an a priori map. To arrive at results for a range of situations, we utilize several different types of environments, and add noise to both the dead-reckoning and the sensors. Analysis s...
Subjects
arXiv: Computer Science::Robotics
free text keywords: Matched filter, Control engineering, Markov process, symbols.namesake, symbols, Artificial intelligence, business.industry, business, Computer vision, Robot, A priori and a posteriori, Markov chain, Kalman filter, Mobile robot, Probability distribution, Computer science
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