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Encyclopedia > Simultaneous localization and mapping

Simultaneous localization and mapping (SLAM) is a technique used by robots and autonomous vehicles to build up a map within an unknown environment while at the same time keeping track of their current position. This is not as straightforward as it might sound due to inherent uncertainties in discerning the robot's relative movement from its various sensors. For other uses, see robot (disambiguation). ... Autonomous robots are robots which can perform desired tasks in unstructured environments without continuous human guidance. ... Not to be confused with censure, censer, or censor. ...

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. The word iteration is sometimes used in everyday English with a meaning virtually identical to repetition. ... The Kalman filter is an efficient recursive filter that estimates the state of a dynamic system from a series of incomplete and noisy measurements. ... Result of particle filtering (red line) based on observed data generated from the blue line ( Much larger image) Particle filter methods, also known as Sequential Monte Carlo (SMC), are sophisticated model estimation techniques based on simulation. ... Monte Carlo methods are a widely used class of computational algorithms for simulating the behavior of various physical and mathematical systems, and for other computations. ...

A seminal work in SLAM is the research of R.C. Smith and P. Cheesman on the representation and estimation of spatial uncertainty in the mid 1980s. Other pioneering work in this field was conducted by the research group of Hugh F. Durrant-Whyte in the early 1990s. Professor Hugh F. Durrant-Whyte is known for his pioneering work on probabilistic methods for robotics. ...

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 achievement of the robotics research in the past decades [1]. Unmanned Aerial Vehicle over Iraq. ... // An Autonomous Underwater Vehicle (AUV) is a robot which travels underwater. ... Two different Mars rover designs. ... To meet Wikipedias quality standards, this article may require cleanup. ...

SLAM can use many different types of sensor to acquire data used in building the map such as laser range finders, sonar sensors and cameras. A laser range-finder, or LIDAR (LIght Detection And Ranging), is a device which uses a laser beam in order to determine the distance to an opaque object. ... This article is about underwater sound propagation. ... Large format camera lens. ...

The Kalman filter is an efficient recursive filter that estimates the state of a dynamic system from a series of incomplete and noisy measurements. ... In robotics and sensors, Monte Carlo localization, or MCL, is a Monte Carlo method to determine the position of a robot given a map of its environment based on Markov localization. ... Result of particle filtering (red line) based on observed data generated from the blue line ( Much larger image) Particle filter methods, also known as Sequential Monte Carlo (SMC), are sophisticated model estimation techniques based on simulation. ... In computer vision, sets of data acquired by sampling the same scene or object at different times, or from different perspectives, will be in different coordinate systems. ...

Results from FactBites:

 From the Ash's (8276 words) Localization is the process of using salient features in the surrounding environment to ensure that the robot has correctly identified its position in its map. The localization procedure is used periodically to correct the errors in the robot's "dead-reckoning" or where it believes itself to be based on measurements of how far each wheel has moved. SLAM stands for Simultaneous Localization and Mapping and is one of the biggest challenges in modern mobile robotics.
 ROBART I, II, III (3745 words) Numerous proximity and ranging sensors were incorporated on the robot to support map generation, position estimation, collision avoidance, navigational planning, and terrain assessment, enabling successful traversal of congested environments with no human intervention. Simply put, if a remote operator has to master simultaneous manipulation of three different joysticks (i.e., one for drive and steering, another for camera pan and tilt, and yet a third for weapons control), the chances of hitting a moving target are minimal. CPE is another method for performing simultaneous localization and mapping (SLAM), based on original work by Lu and Milios (1997), who showed that information from a robotÂ’s encoders and laser sensors could be represented as a network of probabilistic constraints linking the successive poses of the robot.
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