Просмотр исходного текста страницы Параллельное составление роботом карты и локализация (SLAM)

Параллельное составлени карты и локализация (Simultaneous localization and mapping = SLAM) это подход, используемый роботами и автономными платформами для построения карты в неизвестном окружении при параллельном отслеживании собственного положения в этом окружении. Это не так прямолинейно, как может показаться, по причине неустранимой погрешности при замере параметров относительного движения мобильного робота.


If at the next iteration of map building the measured distance and direction travelled has a slight inaccuracy, then any features being added to the map will contain corresponding errors. If unchecked, these positional errors build cumulatively, grossly distorting the map and therefore the robot's ability to know its precise location. There are various techniques to compensate for this such as recognising features that it has come across previously and re-skewing recent parts of the map to make sure the two instances of that feature become one. Some of the statistical techniques used in SLAM include Kalman filters, particle filters (aka. Monte Carlo methods) and scan matching of range data.


A seminal work in SLAM is the research of R.C. Smith and P. Cheeseman on the representation and estimation of spatial uncertainty in 1986.[1][2] Other pioneering work in this field was conducted by the research group of Hugh F. Durrant-Whyte in the early 1990s.[3]


SLAM in the mobile robotics community generally refers to the process of creating geometrically accurate maps of the environment. Topological maps are another method of environment representation which capture the connectivity (i.e., topology) of the environment rather than creating a geometrically accurate map. As a result, algorithms that create topological maps are not referred to as SLAM.


SLAM has not yet been fully perfected, but it is starting to be employed in unmanned aerial vehicles, autonomous underwater vehicles, planetary rovers and newly emerging domestic robots. It is generally considered that "solving" the SLAM problem has been one of the notable achievements of the robotics research in the past decades. [4] The related problems of data association and computational complexity are amongst the problems yet to be fully resolved.


SLAM can use many different types of sensors to acquire data used in building the map such as laser rangefinders, sonar sensors and cameras.