Simultaneous localization and mapping
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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 its current position. This is not as straight forward as it might sound due to inherent uncertainties in discerning the robot's relative movement from its various sensors.
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 cummulatively, 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.
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.