Monte Carlo localization
Encyclopedia
In robotics
and sensors, Monte Carlo localization (MCL) is a Monte Carlo method
to determine the position of a robot given a map of its environment based on Markov localization. It is basically an implementation of the particle filter
applied to robot localization, and has become very popular in the Robotics literature. In this method a large number of hypothetical current configurations are initially randomly scattered in configuration space
. With each sensor update, the probability that each hypothetical configuration is correct is updated based on a statistical model of the sensors and Bayes' theorem
. Similarly, every motion the robot undergoes is applied in a statistical sense to the hypothetical configurations based on a statistical motion model. When the probability of a hypothetical configuration becomes very low, it is replaced with a new random configuration.
Robotics
Robotics is the branch of technology that deals with the design, construction, operation, structural disposition, manufacture and application of robots...
and sensors, Monte Carlo localization (MCL) is a Monte Carlo method
Monte Carlo method
Monte Carlo methods are a class of computational algorithms that rely on repeated random sampling to compute their results. Monte Carlo methods are often used in computer simulations of physical and mathematical systems...
to determine the position of a robot given a map of its environment based on Markov localization. It is basically an implementation of the particle filter
Particle filter
In statistics, particle filters, also known as Sequential Monte Carlo methods , are sophisticated model estimation techniques based on simulation...
applied to robot localization, and has become very popular in the Robotics literature. In this method a large number of hypothetical current configurations are initially randomly scattered in configuration space
Configuration space
- Configuration space in physics :In classical mechanics, the configuration space is the space of possible positions that a physical system may attain, possibly subject to external constraints...
. With each sensor update, the probability that each hypothetical configuration is correct is updated based on a statistical model of the sensors and Bayes' theorem
Bayes' theorem
In probability theory and applications, Bayes' theorem relates the conditional probabilities P and P. It is commonly used in science and engineering. The theorem is named for Thomas Bayes ....
. Similarly, every motion the robot undergoes is applied in a statistical sense to the hypothetical configurations based on a statistical motion model. When the probability of a hypothetical configuration becomes very low, it is replaced with a new random configuration.
External links
- F. DellaertFrank DellaertFrank Dellaert is an Associate Professor in the School of Interactive Computing at the Georgia Institute of Technology. He is also affiliated with the RIM@GT center and is well-known for contributions to Robotics and Computer Vision.-Early Education:...
, D. Fox, W. BurgardWolfram BurgardWolfram Burgard is a German roboticist. He is a full professor at the Albert-Ludwigs-Universität Freiburg where he heads the Laboratory for Autonomous Intelligent Systems...
, and S. ThrunSebastian ThrunSebastian Thrun is a Research Professor of Computer Science at Stanford University and former director of the Stanford Artificial Intelligence Laboratory . He led the development of the robotic vehicle Stanley which won the 2005 DARPA Grand Challenge, and which is exhibited in the Smithsonian...
, "Monte Carlo Localization for Mobile Robots", IEEE International Conference on Robotics and Automation (ICRA), 1999 - D. Fox, W. Burgard, F. Dellaert, and S. Thrun, Monte Carlo Localization: Efficient Position Estimation for Mobile Robots, Proc. of the Sixteenth National Conference on Artificial Intelligence (AAAI'99)
- Thrun, S., Fox, D., Burgard,W., and Dellaert, F., Robust monte carlo localization for mobile robots, Artificial Intelligence, 128(1-2):99–141